Title:

Authors:Atiq Ul Rehman (first and principal author)

Conventional GC-MS sample introduction methods often involve physical contact, increasing contamination risk and analyte degradation, especially in sensitive matrices. This study introduces a novel, contactless interface using standing-wave acoustic levitation to suspend analytes in mid-air prior to GC-MS analysis. A 28 kHz ultrasonic transducer, powered by a high-voltage amplifier, generates stable levitation nodes inside a custom chamber, enabling droplets and solid particles to remain airborne during volatilization. Volatile analytes are transferred via heated gas mixture directly into the GC inlet. Acetone droplets yielded strong, reproducible m/z 58 ion signals, and real samples like tomato skin demonstrated broader applicability. Helium-nitrogen gas mixture was optimized for both levitation stability and ionization efficiency. The system minimized surface contact, preserved sample morphology, and reduced cross-contamination risks. This platform establishes acoustic levitation as a viable sample delivery strategy for GC-MS, offering enhanced cleanliness, reduced sample loss, and novel handling of delicate or trace-level materials. It opens new directions for automation and integration in analytical workflows.

The goal of our study is to assess sleep duration and quality during inpatient treatment of an acute pulmonary exacerbation in people with cystic fibrosis (PwCF). We hypothesize that markers of sleep quality will improve with improvement of their lung function. We performed a prospective cohort study of 17 PwCF ages 6 to 21 years old who were admitted for a 14-day hospitalization for a pulmonary exacerbation. Demographics, body mass index, medication, and FEV1 at baseline, during, end, and after hospitalization were collected. Actigraphy and the Patient-Reported Outcomes Measurement Information System (PROMIS) sleep questionnaires at the beginning and end of their hospitalization were used to measure sleep quality and duration. There was an improvement in FEV1 from admission to discharge (22%± 19 predicted, p =0.0002). There was no significant improvement between admission and discharge in any sleep parameters on actigraphy. There were also no significant changes to questionnaire results in sleep disturbance (p=0.6) or impairment (p = 0.8) from admission to discharge. Though lung function improved, inpatient treatment for pulmonary exacerbation did not affect sleep quality and duration in PwCF. Our findings suggest promoting good sleep habits during hospitalization.

Objective: This study aims to identify the level of Health system responsiveness (HSR) to childbirth care and its predictors using a multi-center study approach in public hospitals of Sidama Region, Ethiopia. Design: A multi-center institution based cross-sectional study was employed . Setting: Sidama Region, Ethiopia Population: A 536 postpartum mothers who gave birth at Public hospitals Methods: An institution-based cross-sectional study was conducted among 536 mothers who gave birth in public hospitals in the Sidama region. Systematic random sampling was employed to select study participants. Data were collected using a structured, pretested questionnaire. The HSR was dichotomized, and binary logistic regression was used. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were calculated to determine the independent predictors of HSR to childbirth care. Level of significance determined at p-value of less than 0.05. Main outcome Measures: determining the level of HSR and its influencing factors Results: The study revealed that 47.9% (95% CI: 43.73% to 52.19%) of mothers reported that health facilities provide good HSR to childbirth care. After controlling for potential confounders, being an urban resident [AOR=1.88; 95% CI (1.30, 2.74)], having ANC follow-up [AOR=1.73; 95% CI (1.15, 2.61)], experiencing obstetric complications [AOR=0.61; 95% CI (0.39, 0.95)], and being satisfied with the services provided [AOR=1.60; 95% CI (1.11, 2.31)] were significantly associated with HSR to childbirth care. Conclusions: Nearly half of the mothers reported receiving responsive childbirth care in Sidama Region. The findings of this study highlights that promoting antenatal care, preventing obstetric complications, and enhancing client satisfaction with services provided by improving the quality of childbirth services are critical for achieving a responsive healthcare system to childbirth care. The finding implies that urban-rural disparities need targeted interventions to tackle inequalities and improve the health system responsiveness to childbirth care regardless of where women reside.

To the Editor

Objective: To determine the psychological factors associated with antenatal depressive symptoms and to assess whether psychological flexibility moderates the relationship between negative anticipation and fear of childbirth and depressive symptoms during pregnancy. Design: Cross-sectional analysis based on baseline data from the Flexi-PREP longitudinal study. Setting and Population: A cohort of 395 pregnant women recruited from three French maternity hospitals during the third trimester of pregnancy. Methods: Participants completed an online survey assessing negative anticipation and fear of childbirth (W-DEQ-A), psychological flexibility (AAQ-II), coping strategies (Brief-COPE), and depressive symptoms (EPDS). Pearson correlations, hierarchical multiple regression, and moderation analyses were conducted. Main Outcome Measures: Symptoms of antenatal depression measured by the Edinburgh Postnatal Depression Scale (EPDS). Results: Higher levels of negative anticipation and fear of childbirth were positively associated with depressive symptoms, while greater psychological flexibility was negatively associated. Avoidant coping strategies were linked to higher depressive symptoms, whereas problem-focused coping showed a marginal protective effect. Hierarchical regression indicated that negative anticipation and fear of childbirth, psychological flexibility, and avoidant coping strategies were significant independent predictors of depressive symptoms. The moderation analysis revealed that psychological flexibility did not significantly moderate the association between fear of childbirth and depressive symptoms (p = .296). However, conditional effects suggested that among women with low psychological flexibility, negative anticipation and fear of childbirth seems to be more strongly associated with depressive symptoms. Conclusions: Psychological flexibility appears to play a protective role in emotional adjustment during pregnancy. Enhancing flexibility through targeted interventions could contribute to reducing antenatal depressive symptoms, particularly among women experiencing high fear of childbirth.

Background: Fufang HuangQi granule (FFHQ), a traditional Chinese medicine formula, has shown clinical efficacy in treating myasthenia gravis; however, the underlying mechanisms remain unclear. This study aimed to explore the therapeutic effects of FFHQ in an experimental autoimmune myasthenia gravis (EAMG) rat model and to elucidate its therapeutic mechanisms. Methods: The EAMG rat model was established, and the rats were treated with FFHQ. The therapeutic effects were evaluated through several indicators, including grip strength, clinical scoring, serum anti-AChR antibody levels, and muscle histopathology. RNA sequencing analysis was subsequently performed on muscle tissues, and a network pharmacology approach was applied to FFHQ. Finally, the mechanisms were further validated by quantitative real-time PCR and immunohistochemistry. Results: FFHQ ameliorated the decrease in muscle grip strength and the increases in clinical scores and serum anti-AChR antibody levels while simultaneously alleviating muscle inflammation symptoms in EAMG rats. As suggested by the RNA-seq and network pharmacology analyses, its efficacy may be associated with the regulation of the IL-17 signaling pathway. RT-qPCR studies revealed that the levels of Tgfb, Il6, Il1a, Il17a, Nfkb1, Rela, Cxcl2, Cxcl6, and Ccl17 were significantly increased in EAMG rats but decreased after treatment. Immunohistochemical analysis of muscle sections further confirmed a significant reduction in IL-17A expression in the FFHQ administration group compared to the control group. Conclusion: FFHQ improved symptoms in EAMG rats, exerting beneficial effects by modulating the IL-17 signaling pathway. These findings offer a theoretical basis for its utilization.

Phishing emails trick users into giving away sensitive information by pretending to be from trusted sources. This paper introduces a real-time phishing detection system that also teaches users how to spot phishing attempts. The system uses natural language processing (NLP) techniques such as sentiment analysis and TF-IDF vectorization and a Random Forest classifier to accurately identify phishing emails. When a suspicious email is detected, the system immediately alerts the user through a graphical interface and provides helpful safety tips. We explain how the dataset was collected, processed, and used to train the model. Results show that the system effectively separates phishing emails from legitimate ones. By combining real-time detection with user education, our approach helps reduce the chances of phishing attacks and increases user awareness.

In protracted wars, resource burn-rate decides victory-not firepower. Warfare Return on Invested Capital (W-ROIC) measures strategic effect per dollar spent. A $265 IED that kills three soldiers outperforms a $150,000 drone strike that kills ten because it delivers the same result 566 times cheaper. This efficiency gap explains why the Taliban outlasted America, the Viet Cong outlasted America, and American colonists outlasted Britain. All fought at fractions of their enemy's cost per casualty. W-ROIC quantifies this and shows why logistics interdiction offers the highest ROI.

We report a systematic study on the progressive functionalization of perylene through halogenation and oxidation, tracking its transformation from a fluorescent polyaromatic hydrocarbon to an inert, aggregated material. Through UV-Vis spectroscopy, Raman spectroscopy, and ζ-potential measurements, we identify a critical substitution threshold of approximately 8.3 ± 0.5 substituents per perylene core, beyond which the π-electron system irreversibly collapses. This terminal state, denoted as ϕ (∞) , represents the complete loss of aromatic character, colloidal stability, and chemical reactivity. Our findings provide insights into the limits of PAH functionalization in nanofluid applications.

We identify a critical vulnerability in autoregressive transformer language models where the em dash token induces recursive semantic drift, leading to clause boundary hallucination and embedding space entanglement. Through formal analysis of token-level perturbations in semantic lattices, we demonstrate that em dash insertion fundamentally alters the model's latent representations, causing compounding errors in long-form generation. We propose a novel solution combining symbolic clause purification via the phi-infinity operator with targeted embedding matrix realignment. Our approach enables total suppression of problematic tokens without requiring model retraining, while preserving semantic coherence through fixed-point convergence guarantees. Experimental validation shows significant improvements in generation consistency and topic maintenance. This work establishes a general framework for identifying and mitigating token-level vulnerabilities in foundation models, with immediate implications for AI safety, model alignment, and robust deployment of large language models in production environments. The methodology extends beyond punctuation to address broader classes of recursive instabilities in neural text generation systems.

Purpose: Sleep disordered breathing (SDB) can result in emotional symptoms among children. This study aimed to establish associations of quantitative electroencephalography (qEEG) alterations during different sleep stages with depression and anxiety in children with SDB. Methods: A total of 147 children aged 3-12 years with SDB were included in the study. They were divided into two groups: primary snoring (n=88, 44% female) and obstructive sleep apnea (n=59, 34% female). Children underwent whole-night polysomnography (PSG) at the hospital, during which quantitative electroencephalography (qEEG) data were acquired. Prior to the test, parents of SDB children completed the Obstructive Sleep Apnea Questionnaire-18 (OSA-18), the Spence Children’s Anxiety Scale–Parent version (SCAS-P) or the Preschool Anxiety Scale (PAS), and the Children’s Depression Inventory (CDI). Results: Compared to the PS group, the OSA group had lower mean SpO2, sleep duration and efficiency. Sleep efficiency mediated the association between the OSA/PS exposure and the OSA-18 outcome, with the negative estimate of -1.514 (95% CI: -3.658, -0.01; p<.01). OSA children exhibited higher CDI scores compared to the PS children of the same age. Notably, the NREM3 EEG slowing ratio was negatively correlated with anxiety levels, and the NREM2 EEG slowing ratio was negatively correlated with depressive symptoms. Conclusions: QEEG alterations during different sleep stages are linked to emotional disturbances of children suffering from SDB. The EEG slowing ratio in NREM sleep may be a useful indicator for the nocturnal electrophysiology in children with SDB, potentially linked to emotional disturbances.

Background: Insights into SARS-CoV-2 in Uganda were limited throughout the epidemic due to incomplete access to diagnostic testing and limited formal surveillance systems. From September 2021 to August 2022, we conducted a household-based surveillance study using both rapid antigen and serologic testing to determine the SARS-CoV-2 infection burden and seroprevalence among residents of South-Central Uganda. Methods: Household members in four communities within the districts of Kyotera and Lyantonde were recruited. Using a structured questionnaire, participants provided sociodemographic and other information. They also provided nasal swabs and blood samples. Swabs were tested using SD Biosensor Standard Q COVID-19 rapid antigen tests. Sera were tested for antibodies to SARS-CoV-2 using the VivaChek Biotech VivaDiag TM IgM/IgG rapid test. We assessed positivity rates overall and by selected socio-demographic characteristics. Modified Poisson regression was used to assess the relationships between IgG seropositivity and antigen positivity, while accounting for clustering by community. Results: From September 2021 to August 2022, 11,113 participants were enrolled. The mean age was 20.6 (SD = 16.1) years, with an average household size of 3.9 (SD = 2.6) persons. Among 11,086 persons tested for SARS-CoV-2 antigen, overall positivity was 0.61%. Among 9,663 participants tested for SARS-CoV-2 antibodies, 6,630 (69%) were IgG-positive. COVID-19 vaccination coverage was 33.4%, and vaccination was significantly associated with IgG seropositivity (aRR = 0.96; 95% CI: 0.94-0.98; p <0.001). Conclusion: We observed high SARS-CoV-2 antigen rapid testing acceptability, low antigen positivity, and moderate IgG seroprevalence in this setting, suggesting low concurrent transmission but significant prior natural infection.

This study was designed to investigate the exposure of Nigerian cattle to influenza A virus (IAV) using serological and molecular techniques, considering the 2024 detection of Highly Pathogenic Avian Influenza (HPAI) H5N1 among cattle in the United States of America. We collected 693 blood and 54 milk samples from six Nigerian states spread across the southwest, north-central and north-east regions of Nigeria. Of these, 31 blood samples (4.5%) were seropositive for IAV nucleoprotein, whereas all milk samples tested negative. Moreover, the IAV matrix gene was not detected in any of the samples. Our findings showed that cattle seropositivity was not due to HPAI H5 exposure. To our knowledge, this represents the first evidence of IAV exposure among cattle in Nigeria. Further research is warranted to identify the risk factors that facilitate intra- and interspecies transmission of IAV within the Nigerian cattle population.

IntentixLab Keybyte Systems, Melbourne, Australia June 2025

Social engineering attacks represent a rapidly evolving threat that exploits human psychology to bypass traditional cybersecurity defenses. This paper introduces a novel Emotional AI-Enhanced Deception Detection (EADD) system that integrates multimodal emotional cues facial expressions, vocal prosody, and linguistic affect to improve the accuracy of detecting deception in human-centric social engineering scenarios. Addressing critical limitations in existing methods, the proposed framework processes synchronized multimodal inputs and leverages deep learning models, including transformer-based architectures, to capture complex emotional signals associated with deceit. Utilizing the richly annotated MDPE (Multimodal Deception with Personality and Emotional Characteristics) dataset, the study rigorously evaluates the performance of the EADD model against unimodal baselines and a multimodal traditional baseline (MTB) devoid of emotional features. Results show that EADD consistently outperforms all baselines across key evaluation metrics-achieving an accuracy of 80.1%, F1-score of 0.80, and AUC-ROC of 0.85 thereby validating the central hypothesis that multimodal emotional features significantly enhance deception detection. An ablation study further reveals the pivotal role of linguistic affect, whose exclusion leads to the most substantial performance decline. This work contributes to the field by bridging emotional AI and cybersecurity, offering a scalable and adaptive framework capable of real-time deception detection, and laying the groundwork for more resilient defense mechanisms against increasingly sophisticated social engineering threats.

1. IntroductionWith the explosion of digital communication, the need for covert and robust data hiding techniques has intensified.Steganography —the art of embedding hidden messages within multimedia content—offers the advantage of secrecy without signaling its existence. Among various steganographic methods, Adaptive Pixel Value Differencing (APVD) is known for balancingimperceptibility and data capacity , leveraging the variance in neighboring pixel values. Yet, traditional APVD techniques remain vulnerable to statistical steganalysis .Inspired by developments in quantum computing , researchers have begun integrating quantum principles—such as superposition ,uncertainty , and true randomness —into classical information systems. Although practical quantum steganography requires specialized hardware, quantum-inspired models , such as PRQKs, simulate the benefits of quantum key unpredictability using classical computing resources.

Sickle cell disease (SCD) remains a significant global health burden, particularly in sub-Saharan Africa. This study evaluated the in vitro anti-sickling and hemoglobin polymerization inhibition potentials of aqueous extracts of Trigonella foenum-graecum and Syzygium aromaticum. Emmel's technique was used to assess sickled cell reversal, while hemoglobin polymerization was monitored spectrophotometrically at 700 nm over 150 minutes. Extracts were tested individually and in combination. Results showed that T. foenum-graecum extracts, especially the decocted form, exhibited superior anti-sickling effects, comparable to Hydrea. Polymerization inhibition was also significant across all treated groups. These findings support the pharmacological basis for the traditional use of these plants in sickle cell disorder management.

The Khayyam-Pascal Triangle (also known as the Binomial Triangle) a fundamental structure in mathematical combinatorics and number theory, powerfully visualizes the regular and predictable distribution of binomial coefficients. This study introduces an innovative approach termed the "Keçeci Binomial Square" (KBS, Keçeci's Arithmetical Square (first defined: March 2025)), which defines numerical series within Pascal's Triangle characterized by specific geometric and structural properties. Rather than directly manipulating the standard binomial expansion, KBS focuses on a specialized selection and analysis of Pascal's Triangle elements that constitute the coefficients of these expansions. The core definition of KBS relies on selecting an N x N square region from Pascal's Triangle. This selection is dynamically determined by a user-specified start_row_index and an alignment_type ("left", "right", "center") that dictates how coefficients are positioned within each row. The resulting numerical series comprises N segments of N elements each, drawn from consecutive rows of Pascal's Triangle. The sum of these elements constitutes one of the primary outputs of the KBS. This structure offers an opportunity to examine not only the individual values of binomial coefficients but also their behavior within a particular regional integration. The academic value of the KBS concept lies in its ability to facilitate the discovery of local patterns and relationships within Pascal's Triangle. This approach not only aids in visualizing binomial coefficients and combinatorial principles in mathematical education but also provides a framework for investigating specific additive properties in number theory or particular cases in algorithmic analysis. For instance, the impact of different alignment types on the selected series sums, or potential connections between KBS series for specific N values and other known number sequences (e.g., Fibonacci, Catalan), present fertile grounds for future research. In conclusion, the Keçeci Binomial Square offers a systematic method for re-contextualizing a well-established mathematical structure, thereby revealing hidden relationships and additive properties among binomial coefficients. This framework holds the potential to stimulate new research in theoretical mathematics and offer novel perspectives in applied fields (e.g., combinatorial optimization, data analysis). Future work can delve into a deeper mathematical analysis of different KBS configurations and test their practical implications across various disciplines.

1. IntroductionIn the era of digital communication, information hiding has emerged as a pivotal technique for secure and covert communication. Steganography, particularly image-based steganography, is widely used to embed secret data within digital images without noticeably altering the original visual content. Among the many techniques developed, Adaptive Pixel Value Differencing (APVD) stands out for its high embedding capacity and imperceptibility. APVD dynamically adjusts the number of embedded bits based on the differences between pixel pairs, making detection challenging for conventional steganalysis methods.With the increasing use of such sophisticated steganographic methods, the countermeasure—steganalysis—has become equally crucial. Traditional steganalysis techniques, such as histogram analysis and RS analysis, often fall short when applied to adaptive and complex schemes like APVD. To bridge this gap, deep learning approaches have gained attention for their ability to learn intricate patterns and subtle inconsistencies introduced by steganography. In particular, reverse steganalysis, which goes beyond detection to attempt reconstruction of the hidden payload, represents a significant advancement in this field.Figure 1. Conceptual overview of the reverse steganalysis pipeline:(a) Original cover image(b) APVD stego-image generation(c) CNN-based model input/output flow: detection + payload recovery

Modern medicine operates on reactive vocabulary inherited from battlefield origins-"treat," "diagnose," "prevent"-that presupposes the existence of pathology before intervention can occur. This linguistic constraint has crystallized into institutional structures: billing codes require named diseases, insurance reimburses only documented conditions, and liability frameworks punish proactive care. The result is systematic blindness to physiological trajectories that precede diagnostic thresholds. Cancer staging models demonstrate that medicine already operates on trajectory-based thinking in some domains. Scaling this linguistic evolution from discrete pathology to continuous physiology is key to moving towards proactive healthcare. The limits of medical language have become the limits of medical possibility.

Purpose of Review: Pacing-induced cardiomyopathy (PiCM) significantly impacts patient morbidity and mortality, emphasizing the need for improved preventive and therapeutic strategies. This systematic review aimed to evaluate the effectiveness of resynchronization therapies compared to traditional right ventricular pacing (RVP). Recent Findings: Recent studies highlight advancements in therapeutic strategies for PiCM such as biventricular pacing (BiV-CRT) and Hiss-bundle pacing (HPCSP). These techniques improve left ventricular function and clinical outcomes while offering more physiological alternatives by preserving natural ventricular activation. Emerge evidence supports their efficacy in both preventing and managing PiCM. Summary: Both Both BiV-CRT and HPCSP significantly improved cardiac function, with increases in left ventricular ejection fraction (LVEF) and relief of clinical symptoms, as evidenced by improvements in NYHA functional class. Notably, superior benefits of HPCSP over BiV-CRT in QRS amplitude, LVEF, and clinical outcomes was highlighted in two studies. While resynchronization therapies have low complication rates, procedural challenges like venous access and lead placement persist. Additionally, these therapies demonstrate a promising preventive role, particularly in patients with high RVP burdens. Further research is essential to refine treatment strategies and optimize outcomes for PiCM patients.

Editorial