Optimising Aspirin Use for Pre‐Eclampsia Prevention: with adherence insight.Short title: Aspirin for Pre‐Eclampsia Prevention with adherence insight.Raya Vinogradov1,2, c,

Despite the enormous efforts and funds spent to find an effective treatment for COVID-19, the results have been disappointing. In previous publications, we have demonstrated the remarkable effect of high-dose colchicine in inhibiting the cytokine storm and preventing multiorgan damage and death. However, this treatment is beneficial only after virus entry into the cell. The question of prophylaxis and entry prohibition should also be explored. We now demonstrate the prophylactic effect of bromhexine hydrochloride (BRH), an over-the-counter, non-invasive, effective, well-tolerated medicine, with proven safety, affordable, and inexpensive on 125 men and women. The effect of BRH is best when given continuously for prophylaxis during peaks in contagion in the wave of COVID-19. Then the probability of infection drops sharply, and if a disease does occur, it proceeds mildly. BRH is also effective when given by inhalation for post-exposure prophylaxis. When COVID-19 manifests itself clinically, the efficacy of BRH drops sharply because the virus is already in the cell. However, BRH inhalations are useful because they limit the spread of the virus and have an anti-inflammatory and possibly antiviral effect.

Title PageName of Journal: Clinical Case ReportsManuscript Type: Case ReportTitle: Passenger Lymphocyte Syndrome Treated with Recipient-Matched Transfusions – A Case ReportAuthors: Hyun Lee, Zahra S Hamedi, Asit Kr PaulFirst Author Information : Hyun Lee, MD, Fellow, Division of Hematology, Oncology, & Palliative Care, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, 23219, USA. hyun.Lee@vcuhealth.org. ORCID ID: 0000-0003-0954-9174Co-Author Information : Zahra Hamedi, MD, Fellow, Division of Hematology, Oncology, & Palliative Care, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, 23219, USA. zahraSadat.Hamedi@vcuhealth.org. ORCID ID: 0000-0002-7527-8782Corresponding Author Information : Asit Kr Paul, MD Ph.D, Associate Professor of Medicine, Division of Hematology, Oncology, & Palliative Care, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, 23219, USA. asit.paul@vcuhealth.org. ORCID ID: 0000-0002-3517-4196Author contributions: Hyun Lee and Zahra S Hamedi contributed to manuscript writing and editing; Asit Kr Paul contributed to conceptualization and supervision; all authors have read and approved the final manuscript.Statements : None of the authors received any funding or have any conflict of interest regarding this manuscript. Written patient consent has been obtained for publication of this manuscript. The patient has provided written consent for manuscript writing and publication.Abstract:Passenger lymphocytes syndrome (PLS) is a rare form of immune hemolytic anemia caused by organ transplantation with red blood cell (RBC) antigen incompatibility. Donor lymphocytes may travel with the graft and produce alloantibody against recipient RBCs to cause post-operative hemolysis. We present a case of PLS that presented with acute hemolysis on post-operative day 8 following a deceased donor liver transplant from O+ donor to A- recipient and rapid tapering of post-operative steroid therapy. Although high-dose steroids and donor-matched transfusion are the typical supportive measures, our patient was treated with steroids and recipient-matched transfusions with each of the transfusion attaining adequate response. The reported case suggests that recipient-matched transfusion may be an acceptable alternative PLS treatment when donor-matched transfusion is not available.Key Words : Passenger Lymphocyte Syndrome, Alloimmune Hemolysis, Autoimmune Hemolysis Anemia, Liver Transplantation, ABO-mismatch Transplantation, Case ReportIntroduction :Post-transplant anemia is a common complication with estimated incidence of 4-28% following liver transplants.1 Among its broad differential is PLS, which is a form of graft-versus-host disease that can be seen after a transplant with RBC antigen incompatibility. ABO-mismatch is the most common type of such incompatibility, whereas Rh-mismatch is seen with lower incidence, and other mismatches of minor RBC antigens are rarely seen. Pathophysiology is centered on donor B-lymphocytes transferring with donor graft then producing antibodies against recipient RBC to cause hemolysis.2Unlike stem cell transplant, solid organ transplant does not have renewable source of the pathogenic B-lymphocyte population, hence the disease is typically self-limited.4 Severe cases nonetheless require treatments to temporize until pathogenic lymphocytes are cleared. Donor RBC matched transfusion and steroids have become the widely accepted PLS treatments based on case reports and series.2 However, PLS’s rarity and variable clinical course remain a hindrance to better understanding the disease and evaluating treatment efficacy. Here, we report a patient who was diagnosed with PLS shortly after the liver transplant and a course of post-operative steroids. Experience from this case will contribute to questioning the relationship between PLS’s variable clinical presentations and nonuniform post liver transplant steroid regimens as well as assessing the viability of recipient-matched transfusion as an alternative management option.

Falls among elderly individuals are a leading contributor to injury and death worldwide. As the aging population continues to rise, there will be an even greater demand for advanced safety solutions to minimize the risk of falls. Therefore, this research presents a Human Fall Detection and Airbag Implementation System designed to provide real-time fall detection and injury prevention. The operation of the system takes place in two phases: first, fall detection, and second, alert generation. In the first phase, the subjects use a smart wearable device with a gyroscope and accelerometers to constantly monitor movement and orientation. The sensor data thus collected is preprocessed and then analyzed using signal processing techniques to distinguish normal activities from a possible fall event. The motion pattern is fed into a machine learning model-i.e., a Random Forest algorithm-that assesses in real-time whether an event is likely to be a fall. In case of a high-confidence fall detection, immediate airbag deployment is triggered to minimize the impact and help protect critical body parts like the head, back, and hips. As for the second phase, the system automatically alerts caregivers and nearby medical services for immediate assistance if no movement occurs, even after a threshold time limit. The AI-based technology not only boosts fall detection accuracy but also reduces false alarms, thus providing a dependable solution for aged individuals, impaired patients, and even workers in hazard-prone environments. The proposed system thus aims to provide a better quality of life for these at-risk individuals, allowing higher levels of independence while preventing and/or reducing the risk of severe fall injuries.

Diabetes mellitus, a metabolic disease marked by persistently high blood sugar, is a major global health concern. Side effects, resistance, and the incapacity to adequately address oxidative stress and related consequences are frequently the limitations of current therapeutic approaches. Current treatment approaches are frequently constrained by resistance, side effects, and the incapacity to adequately address oxidative stress and its related consequences. The structural diversity, biocompatibility, and biological pathway modulation of Schiff base metal complexes (SBMs) have made them attractive candidates in the hunt for new antidiabetic drugs. Multiple modes of action are demonstrated by these complexes, such as insulin-mimetic activity, inhibition of important enzymes including α-amylase and α-glucosidase, and antioxidant qualities that reduce oxidative stress. In the context of treating diabetes, recent developments in the synthesis, design, and biological assessment of SBMs have been modelled using ANFIS. Their structure-activity connections, pharmacological potential and clinical translation prospects are highlighted. The results highlight the minimum error values, high correlation coefficient R 2 as 0.90, and function of SBMs as cutting-edge strategies to treat diabetes mellitus’s complicated nature.

Patterns of genetic diversity offer insights in the past history of species and their future evolutionary potential. The Italian peninsula is a widely recognised genetic diversity hotspot and one of the main glacial refugia for European temperate trees, which are expected to be severely threatened by climate change. Here, focusing on the wych elm (Ulmus glabra Huds.) in its Italian range, we combined whole plastome-based phylogeography with lineage-based ecological niche models (ENMs), aiming to: 1) identify the main phylogenetic lineages and past glacial refugia along the Italian peninsula; 2) evaluate the future response of the different lineages to changing environmental conditions; 3) develop effective conservation measures for the species, integrating genetic and ecological information. We analysed 75 wych elm trees across the whole Italian range, finding 42 haplotypes clustered in three main haplogroups: 1) ‘Alpine’; 2) ‘North-central Apennines’ and 3) the ‘South-central Apennines’. The high genetic diversity and significant Nst and Gst statistics (0.867 and 0.086, respectively, p≤0.01) revealed a strong phylogeographic pattern with a deep separation between the Alps and Apennines, suggesting a multiple refugia scenario. In the South-central Apennines, the higher nucleotide diversity suggested a ‘refugia-within-refugia’ pattern. Niche overlap and equivalency test highlighted differentiation between Alpine and Apennine wych elm niches (D=0.178). ENMs for the two main lineages predicted a future decrease in habitat suitability mainly for areas in South-central Italy, raising concern for their diverse and peculiar haplotypes. Our findings may inform effective long-term preservation strategies of the wych elm in one of its genetic diversity hotspots.

Root-knot nematodes are ubiquitous parasites with an amazing ability to interact with a wide variety of plant species. Meloidogyne species are recognized worldwide as a major cause of yield limitation in crops. In two Colombian crops, lulo and plantain, root-knot nematodes were identified using morphometric and molecular methods, with the latter using amplification and sequencing of mitochondrial DNA NADH dehydrogenase subunit 5 genes. For M. arenaria, two haplotypes were identified, one associated with plantain and the other with lulo. To select a smaller set of variables for discriminating crops, an algorithm was developed using Hotelling's T2 test in different scenarios generated by the matrix of variances and covariances of the statistic, as well as by the method of estimation. The most important result was associated with the ability to discriminate haplotypes of nematodes species using relationships between body length, tail length and body diameter at the anal opening, which can simplify the complexity of morphometric measurements to characterize banana and lulo crop nematodes.

Many North American game animals experienced severe population declines during the 19th century due to market hunting. However, estimates of the timing and magnitude of these declines often relies on anecdotal evidence, which makes it difficult to understand the lasting impacts of hunting pressures versus climate or landscape changes on the genetic diversity of contemporary populations. Historical reports suggest the California quail (Callipepla californica) suffered more significant hunting pressure in the late 19th century relative to either Gambel’s (Callipepla gambelii) or mountain quail (Oreortyx pictus). Genomic data can help illuminate the extent to which historical exploitation molded the genetic health of modern quail populations. We compared whole genome sequences from these three quail species to evaluate whether reported differences in hunting pressure affected contemporary patterns of genetic diversity. Contrary to our expectations, California quail did not exhibit any evidence for population declines until the late 20th century, long after the era of market hunting ended. California quail also exhibited the highest levels of genetic diversity across most analyses with evidence for population expansion over the past 500,000 years. In contrast, Gambel’s quail appears to have suffered a recent bottleneck in association with a major drought that impacted the desert southwest during the mid-20th century. Gambel’s quail also exhibited increased realized genetic load for mild and moderately deleterious genetic variants. Together, our results demonstrate that market hunting had little lasting impact on the genetic diversity of these quail species, whereas landscape and climate changes have led to fluctuations in effective population size (Ne) and the buildup of genetic load.

New morphological and molecular data for adult trematodes of Asymphylodorinae from the intestines of two fish species, Leuciscus idus and Tinca tinca from the European part of Russia were obtained. These worms were characterised with conventional morphological technique and by analyses of the 28S rRNA gene sequence variation. Based on molecular data, discovered parasites belong to three known species: Parasymphylodora markewitschi, P. parasquamosa, and Asymphylodora tincae. However, between specimens of each revealed species, except P. parsquamosa, significant morphological differences were detected. Accepting the identity of trematode specimens at molecular level, morphologically different trematodes were recognised as having different morphotypes within both species, including five morphotypes for P. markewitschi and three morphotypes for A. tincae. Results of the present study of European asymphylodorine species morphological and molecular variation considerably differ from that for Far Eastern species, studied earlier. A hypothesis for explanation of this difference was proposed and discussed.

Purpose: To assess reproducibility (interobserver variability and agreement) of Global Work Efficiency (GWE) and left ventricular ejection fraction (LVEF), assessed by 2D-echocardiography (2-DE) and 3D-echocardiography (3-DE), in patients with acute coronary syndrome (ACS), through measurements performed by operators with different levels of experience. Methods: Echocardiographers with three levels of experience were involved in offline processing: advanced, who performed image acquisition – Reader 1 (5 years of training in 2-DE, 3 years in 3-DE); intermediate – Reader 2 (3 years of training in 2-DE, 1 year in 3-DE); beginner – Reader 3 (1 year of training in 2-DE, 3 months in 3-DE). Measurements of GWE and LVEF were performed independently. Interobserver variability and agreement between readers were compared using Bland-Altman plots, as bias and limits of agreement (LOA), and Pearson correlations. Results: 90 patients (54±9 years, 75 males) with ACS were analyzed. Comparing measurements of GWE, following bias and limits of agreement (LOA) were obtained: R1 vs R2: bias -0.6, LOA -3;2 (r=0.98, p<0.001); R2 vs R3: bias -0.3, LOA -3;5 (r=0.96, p<0.001); R1 vs R3: bias -1, LOA -5;4 (r=0.94, p<0.001). Interobserver variability and agreement of GWE were comparable with LVEF by 3-DE: R1 vs R2: bias 0.6, -2, and -1.4; LOA -3;4, -7;4, and -6;4, respectively (r=0.96, 0.91, 0.94, respectively, all p<0.001); however, for LVEF by 2-DE interobserver variability was higher, while agreement was lower. Conclusion: Reproducibility of GWE in patients after an ACS is independent of experience of the operator, and similar with the reproducibility of LVEF by 3-DE.

In Sri Lanka, village chicken can be identified in household backyard systems and Sri Lankan junglefowl which is endemic for the country can be identified in isolated wild areas. Both two species are categorized under genus Gallus and well adapted to harsh tropical environments. Identification of the unique genomic variants of these environmentally adapted species is an important step for developing country specific commercial poultry lines which can survive under harsh conditions while maintaining high productivity. Hence, this study was conducted to identify the whole genome variants of Sri Lankan village chicken and Sri Lankan junglefowl. 16 local chickens (eleven Sri Lankan village chickens and five Sri Lankan junglefowls) were investigated in this study. Genome sequencing was performed on the Illumina HiSeq 2000/2500/X platforms and the resulted quality-controlled short-read sequences (FASTQ reads) had more than 98% mapping rate with mean sequencing depth of 14.9× ±1 bases against the reference genome: GCF_016699485.2. Over 26.1 million single nucleotide polymorphisms and over 3.3 million Insertions and Deletions were identified for two species with the joint variant calling. Principal Component analyses and the phylogenetic analysis confirmed the distinct genetic differentiation between the two species. Fst and Runs of Homozygosity based selection signatures analysis revealed 432 unique genomic regions under selection for junglefowl and 58 unique genomic regions under selection for village chicken. These findings will contribute to identify genomic linkages between different chicken populations and pave the way of applying those in the development of novel commercial poultry lines.

ABSTRACT Surveillance of bat coronaviruses (CoVs) is of public health importance, as accumulating evidence suggests that bats are hosts of the three significant pandemic viruses, namely Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and SARS-CoV-2. Studies focused on identifying different bat CoVs species provides essential insights for preventing and controlling emerging zoonotic diseases. This systematic review evaluated molecular studies on bat CoVs, retrieving 890 articles from PubMed and Google Scholar. After applying selection criteria, 127 articles were included for full-text analysis. Among 54 countries studied, China accounted for the highest number of studies (26%, n = 33). The sample categories consisted of faecal, urine, guano, blood, tissues, oral, and rectal swabs. The molecular detection approaches included polymerase chain reaction (PCR) using species-specific, genus-specific, or broad-range primers. PCR assays targeting the partial RdRp gene (123–440 bp) were used in 94.5% (n = 120) of studies, followed by sequencing with Sanger or next-generation technologies. Full genome sequencing was only performed in approximately 33.9% (n = 43), with metagenomics approaches being used in 15.7% (n = 20) of the studies. The higher positivity rate of bat CoVs were detected in Asia. Globally, the most predominant bat species which tested positive for CoVs were Rhinolophus, Myotis, Miniopterus, Scotophilus, Eidolon, Chaerephon, Hipposideros, and Desmodus. Continuous bat coronavirus surveillance using molecular methods and full genome sequencing is of utmost importance in detecting and characterizing viruses at molecular level and establishing the genetic diversity of new and circulating viruses.

Hybridization is a widespread evolutionary process and a key source of evolutionary novelty. However, despite intensive study, the extent to which hybridization is deterministic and repeatable—particularly in recurrent contact events involving the same species under varying ecological conditions—remains unclear. Here, we investigated three replicated contact zones between Scots pine (Pinus sylvestris) and dwarf mountain pine (Pinus mugo) in Central Europe: two occurring in analogous peatland habitats and one in a contrasting sandstone outcrop. Using genome-wide SNP genotyping of over 1,300 individuals, we analyzed genomic structure, diversity, and ancestry patterns across these zones. All sites revealed pervasive hybridization, dominated by later-generation hybrids and a notable scarcity of pure P. mugo. Across environments, hybrid populations exhibited strikingly consistent genomic compositions, with asymmetric introgression strongly biased toward P. mugo ancestry—suggesting that hybrid genome structure may follow predictable patterns under similar ecological conditions. Nonetheless, we also detected site-specific differences in hybrid diversity and phenotype, highlighting the influence of local environmental selection on shared hybrid genomic backgrounds. We provide genomic evidence that Pinus uliginosa—a morphologically distinct peat bog pine traditionally regarded as a relict and endangered species—is instead an incipient hybrid taxon. Its genome reflects partial stabilization through hybridization and ecological filtering, yet it lacks sufficient genetic divergence to be recognized as a distinct species. Together, these results provide evidence for the repeatability of hybridization processes, which result in the formation of phenotypes reflecting a species continuum subjected to strong environmental pressures. The findings support the simplification of taxonomic nomenclature within the Pinus mugo complex, informing adaptive conservation strategies and the genetic management of hybrid lineages.

Qutaiba I. Ali

Are modern treatments enough? There are treatments such as radiation therapy and chemotherapy, however, they cannot fully treat the ailment. Nevertheless, there are limitations with those cancer treatments because they target all cells and are not specific, have severe side effects, and have limited persistence. The promising nature of immunotherapy, especially for blood cancers, could have the same effect on solid tumors if refined and enhanced. Therefore, this concept of adaptive T-cell evolution would solve the obstacle of tumor immune evasion by allowing T-cells to evolve in real time, similar to how bacteria adapt under selective pressure. Self-editing T-cells would continuously reinforce their receptors and functions to counteract emerging tumor mutations and improve their effectiveness and longevity. This approach could create a universal and reliable approach to cancer treatment and more adequate personalized therapies. Moreover, this could usher in a new path of fully curing cancer as a whole. In a study by Dimitri et al., CRISPR-Cas9 technology has led to a surge in applying genome editing approaches to combat genetic disorders and cancers. The technology can induce specific mutations in the setting of in vitro and in vivo experimental disease models to study various interventional strategies. (Dimitri et al., 2022) This concept can be promising and change the trajectory of cancer treatment to be improved. Evolving T-Cells to Outsmart Cancer Cancer is an inflammatory disease and is a constant challenge for many people regardless of age, ethnicity, country of origin, or lifestyle. Immune cells already help to rid the body of potential threats. For instance, immune cells help to break down ink particles in tattoos over time. This same function of immune cells is utilized against cancerous cells. Unfortunately, there is no sure way of avoiding cancer as it is prevalent, and there is a dire need for better

While urban vascular plant communities are increasingly understood, urban moss communities remain largely unknown. This study investigates grassland bryophyte community composition against various urban environmental gradients in Strasbourg, France. A total of 58 grasslands, ranging from lawns to meadows and wastelands, were analysed for species richness, frequencies, and the proportions of acrocarpous and pleurocarpous species in relation to landscape fragmentation, soil parameters, and management pressures. Urban bryophyte diversity increases with soil aridity and decreases with soil fertility, particularly for acrocarpous species. The impact of vegetation management on mosses varies according to the practices involved, with higher diversity observed in wastelands and reduced richness in lawns. The ratio of acrocarpous species declines as the cover of vascular plants increases. Ultimately, urban environments, including wastelands, should be more carefully considered in regional moss diversity inventories.

Distinguishing glioblastoma (GBM) from secondary brain metastases (BMET) remains a common diagnostic challenge due to overlapping radiographic features and diverse clinical presentations. Non-invasive liquid biopsies offer a promising alternative to image-based diagnostics to guide neoadjuvant treatments and improve patient outcomes. Extracellular vesicles (EVs), membranous nanoparticles, serve as valuable biomarker reservoirs with unique properties, making them ideal for liquid biopsy development. Here, we describe a standardised method to isolate circulating-EVs from 1 mL plasma for tandem high-dimensional proteomic and transcriptomic profiling by data-independent mass spectrometry and next-generation-sequencing, respectively. In this pilot study, a machine-learning based biomarker discovery approach identified GBM- and BMET-specific biomarker signatures, comprised of a panel of 23 proteins and 4 microRNAs that could accurately distinguish GBM and BMET, achieving a high discriminatory power with area under the curves (AUCs) of 0.99 and 0.912. Notably, further analysis of the BMET specimens demonstrated that plasma-EV protein signatures can stratify BMET patients based on the primary tumour malignancy. The performance of the plasma-EV biomarkers illustrates the promising role of EV-based liquid biopsies for minimally invasive and highly accurate brain tumour diagnostics and stratification prior to surgery. However, further validation in larger independent cohorts is warranted to establish their clinical utility.

AbstractProper exercise execution is critical for enhancing performance and reducing the risk of injury in athletic, fitness, and rehabilitation settings. Traditional exercise correction methods, such as human coaching and biomechanical laboratory assessments, often fall short due to limitations in accessibility, subjectivity, and cost. Recent advances in machine learning (ML) and artificial intelligence (AI) offer promising alternatives by enabling real-time movement analysis and feedback through wearable sensors and computer vision-based systems. This review explores the current landscape of ML applications in exercise correction and injury prevention, analyzing 22 peer-reviewed studies that incorporate deep learning models, sensor-based activity recognition, and automated feedback mechanisms. The findings reveal that ML-driven solutions achieve high levels of accuracy in detecting posture deviations and injury-prone movements, while also providing scalable and individualized feedback to users across diverse environments. Despite these advancements, significant challenges remain in the standardization of data inputs, adaptability to biomechanical variability, and ethical concerns related to user privacy and algorithmic bias. Nonetheless, the integration of AI into exercise science holds considerable potential to democratize access to high-quality training and rehabilitation, offering a paradigm shift toward more intelligent, responsive, and injury-resilient movement systems.1. IntroductionIn sports, fitness, and rehabilitation, proper exercise form is essential for both performance and injury prevention. Patients with musculoskeletal injuries due to incorrect exercise often reported lower efficiency of athletic activity and prolonged recovery periods (An et al., 2024). It is especially important for athletes, rehabilitation patients, and fitness enthusiasts to maintain biomechanical health since deviations in movement patterns may lead to chronic injuries (Lin & Wang, 2024). Exercise injuries are prevalent; it is estimated that about 50% of all sports injuries are preventable with appropriate technique and modified training interventions (Patel et al., 2012). Such injuries result in higher healthcare expenditure, lower engagement with physical activity, and chronic impairment of the ability to perform tasks (An et al., 2024). And bad squat form hurts knee ligaments, while bad deadlifting form hurts lower backs. The correction is also important in light of the high incidence of overuse injuries seen in endurance sports (Patel et al., 2012). Traditional exercise correction relies on human guidance that involves coaching, physical therapy, and biomechanics laboratories, where feedback is provided based on visual and tactile inspection from a human expert (Patel et al., 2012). Although these methods are effective, they have shortcomings, such as the importance of reliance on subjective human analysis in motion analysis, less accessibility to remote or underserved individuals and the high cost of biomechanics lab assessments. Biomechanics labs can use motion capture systems, force plates and electromyography (EMG) to analyse movement patterns; however, these technologies are expensive and useless outside the research institutions (Patel et al., 2012). This underscores the need for scalable, cost-efficient solutions, for example, ML-based exercise correction systems. Machine learning (ML), artificial intelligence (AI), and sensor technologies are enhancing exercise monitoring by allowing for movement quality analysis and feedback techniques (An et al., 2024). Also, the popular use of wearable motion sensors, AI video analysis and deep learning algorithms can help people get real-time feedback on the exercise posture of their workout and exercise with a lower risk of injury and higher efficiency (Lin & Wang, 2024). For example, PoseNet and OpenPose are, AI computer vision models that track the skeleton keypoints and inspect wrong movements for exercises like lunges and squats (LIN & WANG, 2024). Wearable-based accelerometers and gyroscopes embedded in fitness tracking devices (e.g., smartwatches, motion analysis suits) also monitor joint angles and movement speed in real-world settings (Patel et al., 2012). Traditional forms of coaching are increasingly being supplemented with AI-based motion analysis solutions that provide: Improved precision with which it identifies movement anomalies, Scalability — users can receive training corrections regardless of where they are, and Automated feedback for individuals who are training in the absence of direct human supervision (Lin & Wang, 2024) Studies show AI-assisted training improves movement recognition accuracy over 90% than manual observation (Lin & Wang, 2024) For instance, AI-enhanced rehabilitation instruments can aid injury recuperating patients by giving specific recommendations tailored from real-time biomechanics data (An et al., 2024). Nevertheless, challenges persist in delivery formats, data variability among individuals, calibration difficulties, and ethical considerations surrounding AI applications in healthcare (Patel et al., 2012). This review aims to give an overview of the past and recent developments in machine learning for real-time exercise correction and injury prevention. Methods include wearable sensors, AI-based motion tracking, and deep learning models. Analyses: Challenges and future directions, such as improving AI performance, addressing biomechanical variability, and promoting real-world accessibility.2. MethodologyThis review adopts a systematic methodology grounded in the PRISMA framework to evaluate the application of machine learning (ML) in real-time exercise correction and injury prevention. A structured search was conducted across five major academic databases, PubMed, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar, for peer-reviewed studies published between January 2015 and March 2025. A combination of Boolean operators was used to search terms including ”machine learning,” ”exercise correction,” ”injury prevention,” ”real-time feedback,” ”pose estimation,” ”wearable sensors,” and ”human activity recognition.” After removal of duplicates, titles and abstracts were screened for relevance. Full-text articles were reviewed against strict eligibility criteria. The study selection process is outlined in accordance with PRISMA standards. Table 1: Inclusion and Exclusion Criteria for Study Selection

Quercus mongolica, a dominant species in northern China, has shown notable range expansion despite intensifying drought. Although highly drought-tolerant, its adaptive mechanisms across geographic drought gradients remain unclear. We sampled 14 populations across a drought gradient to examine leaf morphological adaptations and their implications for water transport and photosynthesis. Leaf area, length, width, and perimeter declined significantly with increasing aridity (p < 0.05). Meanwhile, leaf allometry shifted adaptively, with increased length-to-width ratios and specific leaf area, yielding narrower, elongated leaves. These structural traits were coupled with higher vein density and light capture, suggesting coordinated hydraulic and photosynthetic adaptation. Changes in leaf size, specific leaf area, and vein density emerged as key drought adaptations. These findings underscore the role of trait plasticity in supporting Q. mongolica’s dominance under drought.

Synthetic microbial community (SMC) hold immense potential for application across various vital fields. However, the rational construction of SMCs still faces considerable challenges. The function of SMC is contingent upon the function of individual species within it and their interactions. But the relationship between the function of the SMC and the functions of the individual species it contains, which we call the ‘interaction effect’, is still unclear. In this study, we categorize the interaction effects of species in SMCs into seven types by comparing the functions of SMCs and their constituent species. These types include linearly related additive effect (AE), non-linearly related synergistic effect (SE), stimulatory effect (STE), strong effect (STRE), interference effect (IE), weak effect (WE), and inhibitory effect (INE). This classification of interaction effect can be used to assess the efficiency of SMC. It is not easy to quickly construct efficient SMC with synergistic effects (SE), although numerous reports document synergistic effects (SE) in SMCs. Moreover, this classification of interaction effect may also apply in other fields such as ecological communities, drug combinations, composite materials.

Understanding how biodiversity affects the stability of ecosystems remains a hot topic in ecology despite significant advances in recent decades. Here, we show that breaking down the two main components of ecosystem stability ---resistance and resilience--- into species-level components makes it possible to define a net biodiversity effect on ecosystem resistance and resilience, just as in traditional biodiversity and ecosystem functioning research. Our approach reveals that biodiversity affects ecosystem resistance through its effects on species resistance, weighted by species relative abundance. More surprisingly, it also reveals that the net biodiversity effect on ecosystem resilience depends not only on species resilience, but also necessarily on species resistance. Finally, we anchor these general theoretical results in the context of grassland ecosystems using a mechanistic model. In particular, we show how functional complementarity, species' traits and life-history strategies determine the effects of biodiversity on ecosystem resistance and resilience.

The eastern Bering is a productive high-latitude ecosystem characterized by high interannual variability in physical environmental conditions that impact biological communities. We investigated how the diet composition of northern fur seals (Callorhinus ursinus) breeding on the Pribilof Islands was influenced by this variation, focusing on water temperatures (surface and bottom) and an index of walleye pollock abundance within foraging areas. We also explored whether interannual variation in diet composition influenced fur seal pup mortality rates or weights. The frequency of occurrence (FO) of seven (of eight) fur seal prey groups detected from hard parts analysis of samples collected from 1987 to 2012 was affected by interannual variation in at least one of the three environmental variables. Of particular note was the decrease in pollock FO as pollock abundance within foraging areas decreased, as pollock is one of the most important fur seal prey species in this region and projected to decline considerably in the coming decades. A considerable amount of variation in FO of almost all prey groups was explained by year effects, suggesting that water temperatures alone were not sufficiently capturing the influences of regional and local physical environmental conditions on prey availability for fur seals. The summed FO of non-pollock prey groups had a small but detectable effect on pup mortality rates, indicating that availability of prey groups beyond just pollock are somewhat beneficial for female northern fur seals early in lactation. Our results suggest that projected environmental changes in the eastern Bering Sea are likely to influence fur seal diets, but predicting the magnitude and direction of such changes is hampered until the underlying drivers of the observed temporal trends are better resolved.

A document by Abdul Moiz Khan. Click on the document to view its contents.

Multiple System Atrophy (MSA) is a rare, progressive neurodegenerative disorder characterized by a combination of autonomic dysfunction, parkinsonism, and cerebellar ataxia. Diagnostic criteria for MSA have evolved significantly, with recent updates incorporating advanced imaging techniques to enhance early and accurate detection. Pathologically, MSA is defined by the presence of α-synuclein-positive glial cytoplasmic inclusions and is increasingly associated with neuroinflammation, dysregulated iron homeostasis, and demyelination. Management of MSA remains primarily symptomatic, with treatment strategies tailored to address both motor and non-motor symptoms. The two main subtypes—MSA with predominant parkinsonism (MSA-P) and MSA with cerebellar features (MSA-C)—present distinct clinical profiles, each requiring nuanced diagnostic and therapeutic approaches. Recent advances in understanding MSA pathophysiology have driven the development of targeted treatments. These include molecular therapies aimed at reducing α-synuclein aggregation and immunotherapies directed against α-synuclein, though their clinical efficacy is still under investigation. Additionally, approaches targeting neuroinflammation and promoting neuroprotection represent promising areas of research. Despite these developments, current treatment remains largely supportive, focused on alleviating symptoms rather than halting disease progression. The underlying cause of MSA continues to be poorly understood. To improve early diagnosis, therapeutic innovation, and patient outcomes, further refinement of diagnostic criteria, broader inclusion in clinical trials, and the collection of long-term clinical data are urgently needed. This review article consolidates current knowledge on MSA, highlighting recent advancements in diagnosis, pathophysiology, and emerging therapeutic strategies to guide future research and clinical practice.