India launched the world's largest health assurance scheme in 2018, declaring it a giant step towards Universal Health Coverage. The concept of Universal Health Coverage, as defined by the High-Level Expert Group in India, entails both protection from financial hardship and addressing social inequities in access to health. India has a remarkable number of health government-funded health insurance schemes. However, these schemes have time and again proven to be a failure in achieving Universal Health Coverage. In this paper, we discuss the evolution of the government-funded health insurance schemes in India over the period and its attempt towards achieving UHC. Given the vast diversity in the demographics of India and various healthcare needs, we attempt to view Universal Health Coverage as a wicked problem. A wicked problem occurs due to the existence of an underlying problem and discrepancies in its representation. The path of Universal Health Coverage in India entails ten central pillars under which policymakers must address the web of problems. We argue that the characteristics defining a wicked problem make Universal Health Coverage arduous for policymakers. And that the path to achieving Universal Health Coverage cannot be a straight one with a government-funded health insurance scheme. This perspective article intends to draw the attention of the policymakers and relevant stakeholders to view achieving Universal Health Coverage beyond the scope of offering means-tested financial protection and impart an interdisciplinary approach to attain Universal Health Coverage.

Heterogeneous catalysts promoting efficient production of reactive species and dynamically stabilized electron transfer mechanisms for peroxomonosulfates (PMS) still lack systematic investigation. Herein, a more stable magnetic layered double oxides (CFLDO/N-C), was designed using self-polymerization and high temperature carbonization of dopamine. The CFLDO/N-C/PMS system effectively activated PMS to remove 99% (k=0.737 min-1) of tetracycline (TC) within 10 min. The CFLDO/N-C/PMS system exhibited favorable resistance to inorganic anions and natural organics, as well as satisfactory suitability for multiple pollutants. The magnetic properties of the catalyst facilitated the separation of catalysts from the liquid phase, resulting in excellent reproducibility and effectively reducing the leaching of metal ions. An electronic bridge was constructed between cobalt (the active platform of the catalyst) and PMS, inducing PMS to break the O-O bond to generate the active species. The combination of static analysis and dynamic evolution confirmed the effective adsorption of PMS on the catalyst surface as well as the strong radical-assisted electron transfer process. Eventually, we further identified the sites where the reactive species attacked the TC and evaluated the toxicity of the intermediates. These findings offer innovative insights into the rapid degradation of pollutants achieved by transition metals in SR-AOPs and its mechanistic elaboration.

Traditional DOA estimation methods include beamforming, maximum likelihood estimation, subspace-based methods and the sparsity-inducing methods, and DOA estimation is made by establishing the relationship between the received signal and the geometric characteristics of the array. However, factors such as low signal-to-noise ratio, low snapshot, array errors, coherent signals, and broadband signals can seriously affect the performance of these methods. Existing improved methods, such as spatial smoothing and compressed sensing to deal with coherent signal sources, and band division technology to deal with broadband signal sources, are often at the expense of resolution. Besides that, traditional methods tend to be poorly extrapolated and fail to make satisfactory estimates in complex situations. In order to deal with the above problems, some studies have proposed machine learning methods and deep learning methods to estimate DOA. However, the generalization ability of machine learning methods is weaker than that of deep learning methods, and most of them only use synthetic data for experiments, which cannot guarantee the performance in practical applications. Most deep learning methods model DOA estimation as a classification problem on grids, which limits the accuracy of estimation results. If the accuracy is to be increased, the grids have to be finer, which significantly increases the computational cost. Like the above machine learning methods, most deep learning methods do not give experimental results on measured data. This paper proposes a novel DOA estimation method based on the Transformer model to solve the DOA estimation problem. Firstly, compared with the traditional Transformer, the model in this paper adds a sensor-based attention mechanism specially designed for DOA estimation. This method abandons the previous grid classification, and directly regards the DOA estimation problem as a regression problem to minimize the error. It can be proved through strict mathematical derivation that its output can be decomposed by pseudo-singular value, and the eigenvalue matrix is the same as that of the MUSIC method, which means that the output of the proposed attention module is in the space spanned by the (projected) signal and noise eigenvectors. If the eigenvalue is large, the spanned space is dominated by the corresponding eigenvector, which forces the model to concentrate on the vital eigenvectors. Secondly, the complexity of the sensor-based attention mechanism is significantly reduced compared with the original attention mechanism, from O(N2) to O(M2), where N is the number of snapshots, M is the number of sensors. Thirdly, we conducted simulation experiments including low signal-to-noise ratio, low snapshot, array errors, coherent signal and broadband signal scenarios, and the results show that our method has good adaptability to various scenarios. Fourthly, in order to verify the practical application ability of our model, we carried out migration and testing on the measured data, and the results show that our method still has a good effect. Fifthly, in order to cope with possible environmental changes in practical applications, we specially set up a generalization setting experiment. This experiment mainly explores the generalization ability of the model for unknown scenarios, including the generalization situation under different signal-to-noise ratios and different array error strengths, and satisfactory results have been achieved. Finally, since our model needs to know the number of sources in advance, and the number of sources is sometimes unknown in reality, we slightly modify the DOA estimation model, changing the regression head to the classification head to realize the estimation of the number of sources. The results show that the average estimation accuracy is about 98%, which further enhance the application capabilities.

Time-frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time-frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both specific spike-continuous-time-neuron (SCTN) based neural architecture and an adaptive learning rule. We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised Spike-Timing-Dependent Plasticity (STDP) learning rule to effectively adjust the network parameters. Unlike traditional methods for time-frequency analysis, our approach obviates the need for segmenting the signal into several frames, resulting in a streamlined and more effective frequency analysis process. Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and  generate a Spikegram akin to the Fast Fourier Transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals demonstrating an accurate correlation to the equivalent FFT transform.

The side-chain has a significant effect on the optical properties and aggregation behaviors of the organic small molecule acceptors, which becomes an important strategy to optimize the photovoltaic performance of organic solar cells (OSCs). In this work, we designed and synthesized three novel nonfused ring electron acceptors (NFREAs) OC4-4Cl-Ph, OC4-4Cl-Th and OC4-4Cl-C8 with hexylbenzene, hexylthiophene and octyl side chains on the π-bridge units. Compared with OC4-4Cl-Ph and OC4-4Cl-Th, OC4-4Cl-C8 with linear alkyl side chain has more red-shift absorption, which is conducive to obtaining higher short-circuit current density. Additionally, the OC4-4Cl-C8 film exhibits a longer exciton diffusion distance and the D18:OC4-4Cl-C8 blend film displays faster hole transfer, weaker bimolecular recombination, and more efficient exciton transport. Furthermore, the D18:OC4-4Cl-C8 blend films can form good nano fibril-like interpenetrating networks, which can facilitate exciton dissociation and charge transport. Finally, OC4-4Cl-C8 based devices can generate an excellent PCE of 16.56%, which is much higher than OC4-4Cl-Ph (12.29%) and OC4-4Cl-Th based (11.00%) ones, being the highest PCE among the NFREA based binary devices. All in all, we have demonstrated that side-chain engineering is an efficient way to achieve high-performance NFREAs.

Our personalities have a big impact on our daily life. It affects how we think, feel, act, and express ourselves and how our mental health is affected. The work will make use of the Myers-Briggs Personality Type Dataset from Kaggle (MBTI). The Myers-Briggs 16 personalities, also known as personality types, are a subset of the MBTI. The four factors—introversion versus extraversion; sensing versus intuition; thinking versus feeling; and judging versus perceiving—are used to classify human personality. The 16 personality types of the MBTI are formed from these four fundamental dimensions. In our work, we apply different machine learning algorithm on the MBTI dataset and do a comparative study with our proposed model based on CNN+BiLSTM along with CPSO optimizer on MBTI dataset. CPSO optimizer is based on the social behaviour of the animals. Based on the idea of animal swarm intelligence displayed in flocks and shoals, this method sought to optimize nonlinear continuous functions. In juxtaposed with other state-of-the-art techniques, the CNN+BiLSTM with CPSO optimizer model outperformed well with 93.85% accuracy, 93% precision, 93% recall, and 89.99% F1 Score.

This study embarks on a comprehensive bibliometric analysis of 442 machine learning articles published between 1989 and 2019. Leveraging keywords such as “machine learning”, “hybrid machine learning”, and “hybrid decision tree” in the Google Scholar database, we collated articles and scrutinized them based on diverse parameters, including the language of publication, year, software employed, number of pages and authors, citation count, keywords, and methodologies utilized. Our research findings spotlight a noteworthy uptick in publications post-2008, predominantly in the English language. The most commonly used machine learning method, such as SVM, KNN, and ANN, have been determined. the most popular software tools like WEKA and MATLAB, have been determined. While feature selection methods, particularly the Genetic Algorithm and Information Gain, are widely used, there exists a marked underutilization of ensemble learning techniques. Within the scope of the study, the most frequently used parameter optimization methods were also determined. This study, which includes important information such as commonly used machine learning techniques, feature selection approaches and parameter optimization strategies, can guide researchers both as a retrospective overview of the development of machine learning and as a guide to increasingly used methods such as ensemble learning.

With many practical applications print human life today such as manufacturing surveillance cameras, analyzing and processing customer behavior, …, the problem of face detection and head pose estimation on digital images is being noticed by many researchers. A large number of proposed deep learning models have state-of-the-art accuracies such as YOLO, SSD, and MTCNN, solving the problem of face detection or HopeNet, FSA-Net, and RankPmodelsodel used for head pose estimation problems. According to big many state-of-the-art methods, the process of this task consists of 2 parts face detection to head pose estimation. These two steps are completely independent and do not share information with each other. This makes the model clear print setup but does not leverage most of the featured resources extracted in each model. In this thesis, we proposed the PoseMultitask model with the motivation to leverage the features extracted from the face detection model, sharing them with the head pose estimation branch to improve accuracy. Also, with the variety of data, the Euler angle domain representing the face is large, our model can predict results in the 360° Euler angle domain. Applying the multi-tasking learning method, the PoseMultitask model can simultaneously predict the position and direction of the human head. To increase the ability to predict the head direction of the model, we change the representation of the human face from the rom Euler angle to vectors from the Rotation matrix.

RECALCITRANT MULTIDRUG-RESISTANT PSEUDOMONAS KERATITIS WITH SUBSEQUENT TRIPLE PROCEDUREAuthors: Ēriks Elksnis1,2,4, Eva Elksne1,3, Olita Lūse1,2, Juris Vanags1,4, Guna Laganovska1,4Affiliations:Riga Stradins University, Riga, LatviaLatvian American Eye CenterChildren’s Clinical University Hospital, Riga, LatviaPauls Stradins Clinical University HospitalCorrespondence to: Ēriks Elksnis. ORCID 0000-0002-7899-8224Riga Stradins University, Dzirciema street 16, Riga, Latvia. LV1007.Data availability statement : We hereby transfer, assign, or otherwise convey all copyright ownership, including any and all rights incidental thereto, exclusively to the journal, in the event that such work is published by the journalConflict of interest disclosure : Authors declare no potential conflicts of interest.Ethics approval statement : All procedures performed involving this case were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Consent statement: Written informed consent was obtained from the patient to publish this report in accordance with the journal’s patient consent policy.KEY CLINICAL MESSAGERecalcitrant Pseudomona aeruginosa keratitis is a challenging case in ophthalmology and can lead to irreversible blindness if not treated properly and in time.KEYWORDS: Pseudomona aeruginosa, recalcitrant keratitis, penetrating keratoplasty.INTRODUCTIONCorneal opacity is the 5th leading cause of blindness and visual impairment, affecting approximately 6 million people worldwide; additionally, it is responsible for 1.5–2.0 million new cases of monocular blindness per year. Among all etiologies (such as infection, trauma, and inflammation), infectious keratitis (IK) is the main cause of corneal blindness, with an estimated incidence ranging from 2.5–799 per 100,000 population years (Ting et al. 2021).Pseudomonas aeruginosa (P. aeruginosa ) is a gram-negative aerobic pathogen that can cause a wide range of infections and is one of the main causative pathogens of bacterial keratitis, especially in contact lens-associated keratitis, potentially leading to sight-threatening complications if not appropriately treated (Hilliam, Kaye & Winstanley 2020).A particular characteristic of multidrug resistant Pseudomonas keratitis is its rapid progression, in which corneal destruction can be completed within 24–48 hours in some of the more virulent bacterial strains (Reynolds & Kollef 2021). Therefore, P. aeruginosa -caused keratitis is often associated with a high-cost long treatment period and poor visual outcomes (Hilliam, Kaye & Winstanley 2020).CASE PRESENTATIONA 60-year-old female presented to a tertiary medical center in May 2021 with progressive vision loss, purulent discharge, photophobia, foreign body sensation, and pain in the left eye (LE).Three weeks prior, the patient developed a foreign body sensation in her LE due to contact lenses remaining unremoved for one night. She had visited her local ophthalmologist and been prescribed topical tobramycin/dexamethasone 3 mg/mL four times daily, cyclopentolate 10 mg/mL twice daily, and dexpenthenolum once daily before bedtime. There was clinical improvement during the first 5 days of therapy; however, severe clinical worsening followed. The patient returned to the outpatient department 8 days after the first consultation, and treatment was changed to topical chloramphenicol/dexamethasone 1 mg/2 mg/mL six times daily, cyclopentolate 10 mg/mL twice daily and oral doxycycline 100 mg twice daily. Despite treatment, the symptoms worsened in the following week.On the presenting day at the tertiary medical center, the best corrected visual acuity (BCVA) was 20/25 in the right eye (RE) and hand-motion (HM) vision in the LE. The RE was unremarkable on initial examination, whereas slit-lamp examination of the LE revealed a massive amount of purulent discharge in the conjunctival fornixes and intense mixed conjunctival injection. A large epithelial defect in the cornea was present, associated with a ring-like stromal infiltrate 3.1 mm in height, which was “soupy” in appearance owing to stromal necrosis. A hypopyon 3.2 mm in height was observed (Figure 1A). Both the lids were swollen and erythematous. As the posterior segment could not be visualized, B-scan ultrasonography of the LE was performed and confirmed a flat retina with no vitritis.Corneal scrapings and cultures were obtained from LE. Cultures were acquired using a sterile cotton-tipped swab and placed in transport medium. The scrapings were placed on a glass slide and, together with the culture, forwarded to a microbiology laboratory.Topical chloramphenicol/dexamethasone was discontinued, and levofloxacin 5 mg/mL eyedrops were initially administered every hour. Additionally, cyclopentolate 10 mg/mL was administered twice daily to control pain and prevent synechia, artificial tear drops were administered to aid wound healing, and oral doxycycline 100 mg twice daily was administered to prevent keratolysis.After 12 hours, the condition worsened; discharge remained in the same amount, however, the hypopyon increased to 5.4 mm in height, with an infiltration spread 360 °around the limbus and significant corneal edema (Figure 1B).Levofloxacin was switched to moxifloxacin 5 mg/mL eye drops every hour. As a result, the amount of purulent discharge reduced, yet the central corneal infiltrate and ulcer remained unchanged, with remarkable stromal necrosis and corneal thinning in the nasal limbs (Figure 1C).On the fourth day from admission, multidrug resistantP.aeruginosa  was isolated from the culture. (Table 1). Based on these results, and after consultation with an infectologist, moxifloxacin was changed to fortified piperacillin 1.5 mg/mL/tazobactam 12 mg/mL eye drops every hour.

A Submandibular Stone of 3 cm Length Obscuring Duct: A Case ReportA Sanjel1, R Subedi1, S Rauniyar1, I Silwal2, P Bidari21. Department of Otolaryngology, 2. Department of AnesthesiaHetauda Hospital, Madan Bhandari Academy of Health SciencesDr. Anup Sanjel, MBBS, MS ENT, Hetauda Hospital, Madan Bhandari Academy of Health Sciences (corresponding author).Dr. Rupak Subedi, MBBS, Hetauda Hospital, Madan Bhandari Academy of Health Sciences.Dr. Somee Rauniyar, MBBS, Hetauda Hospital, Madan Bhandari Academy of Health Sciences.Dr. Prashant Bidari, MBBS, MD Anesthesia, Hetauda Hospital, Madan Bhandari Academy of Health Sciences.Dr. Ishwor Kumar Silwal, MBBS, MD Anesthesia, Hetauda Hospital, Madan Bhandari Academy of Health Sciences.

In this issue of Molecular Ecology, Salter et al. (2023) discovered that the Cuban Northern Bobwhite subspecies, Colinus virginianus cubanensis (Gould, 1850), is an ancient hybrid population formed due to historical hybridization between lineages from divergent spatiotemporal origins. Slater et al. sequenced genomes extracted from contemporary samples of Northern Bobwhites, as well as historical museum specimens with the oldest specimens dated in 1859. With this data, they reconstructed the evolutionary origin and history of C.v. cubanensis by combining historical literature with demographic modeling. The demographic model revealed the hybrid origin of the C.v. cubanensis from hybridization between divergent lineages. The Bobwhite first arrived in Cuba between the 12th and 16th centuries from Southern Mexico. Then the secondary gene flow from the Southeastern USA occurred between the 18th and 20th centuries (Figure 1). C.v. cubanensis are hybrids with mixed ancestries while possessing unique genetic variants, which confirm the subspecies status. Hybridization can be a creative process generating and maintaining genetic diversity over space and time.

Cold-adapted species endangered by global change are crucial cases for understanding range dynamics and its interface with conservation. In view of climate change and their sensitivity, Alpine insects should modify their distribution by reducing ranges, while being unable of sufficient displacements and mostly moving uphill. To test these hypotheses, we targeted four threatened, high-altitude bumblebees differing in subgenera and elevation ranges, and covering the main central and south European mountains. We performed species distribution models including climate and habitat, and we described elevation uphill and the year of change with broken-line regressions. Results indicate that climate change will cause severe future range contractions across large areas, more in the Apennines (80% - 85% ca) than the Alps and Pyrenees (24 - 56% ca), with mostly concentric retreats as future extents will nearly entirely be included in the present ones. Remarkably, since the ‘80s elevation uplift has started by about 325 - 535 m, a period coinciding with the beginning of the main warming, and will continue. The size and distribution of climate refugia will challenge conservation: they will be small and context specific (2-60% of current areas), but while in the Apennines and Pyrenees they will be nearly entirely within Protected Areas, only a third will be so for the Alps. Such impressive distribution changes demonstrates that cold-adapted bumblebees can accurately track climate change and be precise sentinels of it, and these results link with the investigated species being specialists with specific habitat requirements of temperature and glacier presence. Overall, the distribution of cold specialist bumblebees driven by climate change demonstrates that conservation should act upon the dynamic realities of species ranges because their range reduction, the impossibility of finding new areas and the movement uphill emerge as consistent patterns.

The effect of climate change on the ecological niche of species represents one of the main threats in maintaining processes and functions in mountain ecosystems. However, these links have rarely been assessed due to the lack of application of ecological niche models at the species assemblage level. Therefore, in this study we integrated species ecological niche modeling techniques to predict assemblage changes in tropical montane systems. We estimate species richness and functional diversity for 2040 and 2060, based on the modeled niche of 39 native tree-shrub-herbaceous species of the montane of Mexico, using a model selection protocol for Maxent and Minimal Volume Ellipsoid (MVE) models in which we selected models based on the statistical significance of the partial ROC test, low omission rates, values of the area under the curve, and the Akaike Information Criterion. We project functional diversity, considering nine functional traits related to primary productivity, rate of decomposition, and natural regeneration. Our results show a high loss rate of species richness and functional diversity at low elevations (1500-2800 m a.s.l.). At high altitudes (2800-3500 m a.s.l.), we find a greater richness of species, without increases in functional diversity. We find that tree species are more likely to maintain their current geographic distribution ranges, while herbaceous species show a large loss of species richness at low and mid-elevations. Therefore, we provide evidence that the effects of climate change will promote imbalances in species assemblage and probably cause a loss of processes and functions, such as soil decomposition and soil organic matter, mainly in low-elevation areas.

Plant pathogens are important for ecosystem functioning and community assembly and respond to a variety of biotic and abiotic factors, which change along elevation gradients. Thus elevational gradients are a valuable model system for exploring how plant community, soil properties, and environmental factors influence pathogens. Yet, how these factors influence pathogens in nature remains poorly understood. We tested patterns and potential mechanisms of plant fungal pathogens along elevational gradients by combining a field survey in the Tibetan Plateau with a global meta-analysis. We found that increasing elevation was associated with a decrease in soil fungal pathogen richness but not foliar fungal disease symptoms. Elevation mainly related to soil fungal pathogen richness through abiotic factors. Whereas no evidence supported association between elevation and foliar fungal disease. The meta-analysis suggests some generality in the results of the field survey: elevation was associated with a decrease in soil fungal pathogen richness, but had no consistent relationship with foliar fungal disease or pathogens. Our study reveals distinct patterns of above- and belowground plant pathogen along elevation gradients and provides new insight into the potential mechanisms in shaping these patterns.

A document by Narendra Pandit. Click on the document to view its contents.

A document by mohadeseh khoshandam. Click on the document to view its contents.

The lossless video coding framework based on HEVC is consisted of prediction and entropy coding, which directly determine the lossless coding efficiency. The initial residuals with large absolute values are directly coded by the lossy coefficient coding techniques in HEVC, which may compromise the lossless coding efficiency. In this paper, an optimized scheme based on transformation and quantization techniques for the HEVC Intra lossless coding is proposed as an additional mode. In the proposed scheme, an initial residual block is divided into two parts: the coefficient block and the second residual block. The coefficient block is derived by the lossy transformation and quantization technics in HEVC. The second residual block is made up with residuals of small absolute values. Both the coefficient block and the second residual block can be efficiently coded by the existing coefficient coding scheme in HEVC. Rate-distortion optimization is employed to choose between the proposed scheme and the transform bypass scheme to achieve the best coding performance for each block. The proposed scheme is implemented into the HM 12.1 software. Experimental results show that the proposed scheme achieves 3.36 percent bit-rate saving on average and up to 12.29 percent compared with the HEVC lossless coding under the Intra main configuration.

To save the communication bandwidth and the missile-borne computing resources, a distributed cooperative guidance law based on the event-triggered mechanism is proposed, which enables the missiles with large differences in spatial location and velocity to achieve simultaneous attacks with only a few dozen information exchanges. The guidance process is divided into two stages. The first stage is the cooperative guidance stage, where missiles achieve consensus of the time-to-go estimates through information exchange. In this stage, each missile is designed with an event-triggered function based on its own state error, and the missile only updates and transmits its information in the communication network when the error meets the set threshold, effectively reducing the occupancy rate of missile-borne resources during the cooperation process. The second stage is the independent guidance stage, where missiles can hit the target simultaneously while keeping the communication network silent. This is achieved by ensuring that the time-to-go estimates of missiles can represent the real time-to-go after achieving consensus. By the design of the two-stage guidance law and the replacement of the event-triggered function, the cooperative guidance system can be ensured to remain stable in scenarios where the leader missile is present and destroyed, and exclude Zeno behavior. The stability of the cooperative guidance law is rigorously proved by algebraic graph theory, matrix theory, and the Lyapunov method. Finally, The numerical simulation results demonstrate the validity of the algorithm and the correctness of the stability analysis.

The purpose of this study is to introduce a generalized Wardowski type quasi-contraction known as α-( ϕ,Ω) in the setting of b-metric space. Firstly, we present typical fixed point results using our new contraction and then we demonstrate the existence of generalized Wardowski type quasi-contraction, supporting the veracity of our findings. Specifically, we illustrate the application of our results through examples. Moreover, we provide a comparison of our result with Nadler’s work to show the efficiency of our main result. The problem related to optimization is solved with the help of this method.

A document by Bryce Buddle. Click on the document to view its contents.

After the vaccine implementation for COVID-19, the WHO set a unified vaccination approach to be adopted by all different countries. However, given the various constraints including vaccine availability, heterogeneous age distributions, and differing control measures across countries, questions arise about the optimality of the WHO strategy. In this study, we develop an age-structured SEIR epidemic model with vaccinated and unvaccinated compartments to optimize age-targeted vaccination strategies for COVID-19, incorporating realistic constraints. The model equilibria and the basic reproduction number $R_0$ are checked. Moreover, mathematical formulation and analysis of optimal control problem, are conducted. The model is calibrated to COVID-19 data and simulated to solve the optimal control problem under various vaccination and distancing scenarios. Results demonstrate that the optimal strategy strongly depends on the population age distribution and contact patterns.  Findings emphasize the significance of age-specific disease transmission in designing vaccination priorities, particularly when vaccine supplies are constrained. The model provides a quantitative framework to inform optimal allocation strategies in a general way that allows to adapt the model for other infectious diseases exhibiting similar features to Covid-19.

This study is aim to determine the direct costs of hospitalisation for stroke inpatients in Pudong New Area, by analysing the composition and trends, and identifying influencing factors, to inform the development and implementation of relevant policies and strategies. This study included hospitalized stroke patients and obtained 1,3061 valid samples. We used a self-designed questionnaire to collect direct cost data for stroke patients from the Regional Health Information Platform of Pudong New Area. We collected information on 28 variables, including admission route, stroke subtype, and costs for hospitalization, consultations, and treatments. The data came from medical institutions in Pudong New Area from 2016 to 2020. The study subjects had an average age of 71.69 ± 13.11 years, with 56.8% being men and 43.2% being women. Among them, 15.5% had haemorrhagic strokes, 84.3% had ischemic strokes, and 0.2% had undifferentiated strokes. The highest costs were attributed to oxygen therapy, medication, and examinations. The results of the study also describe trends in the movement of various types of costs. In Pudong New Area, Shanghai, hospitalized stroke patients, particularly older men after an ischemic stroke, have higher likelihood. The main cost factors include oxygen therapy, examinations, and medications. It is crucial to pay attention to the trends in the costs of oxygen therapy, nursing, and medications. Age, length of stay, and stroke subtype have an impact on the total hospitalization costs, emphasizing the importance of the pre-treatment and recovery periods for cost reduction efforts.

To the Editor,Non‐specific Lipid transfer protein syndrome ( nsLTPs) : case series from the west of IrelandZainab Almossalli1, Hajer Mohamed1, Lynette Frain 1, Cariosa Lee-Brennan1University Hospital Galway1

Introduction Tuberculosis (TB) in Nigeria is a serious disease that is fast becoming deadly. In Nigeria, the burden and mortality of the disease are high, making it one of the infectious diseases with a significant public health challenge. Aim This paper is aimed at examining the prevalence of TB in Nigeria, with emphasis on one of its strains, namely XDR-TB, and factors promoting the disease and its strains, among others. Methodology A literature search was done about Tuberculosis in Nigeria using the keywords; ‘Drug Resistance; Factors; Infection Rate; Mortality Rate; Nigeria; XDR-TB’ through databases such as PubMed, ResearchGate, Google Scholar and World Health Organization between a 10-year-period. Result We found that Nigeria is among the 10 countries in the world with very high tuberculosis incidence and mortality. A number of factors aid the prevalence of the disease in Nigeria: poverty, malnutrition, overpopulation and overcrowding, ignorance, stigma and discrimination, etc. When it comes to the diagnosis of tuberculosis, Nigeria encounters a lot of challenges. For instance, the country lacks access to quality diagnostic facilities, the healthcare system is ill-equipped, and a good number of the populace lacks the necessary awareness about the dangerous nature of the disease. Conclusion The Nigerian government, health workers, key stakeholders, and communities, both affected and non-affected, should all come together to fight off Tuberculosis and its spread in Nigeria. In order to be successful, the eradication effort against TB in Nigeria has to permanently eliminate the causative agent and risk factors everywhere in the country as pathogen prevalence is globally reduced to zero, hence removing the risk of re-infection.