OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.

A dual-antigen self-amplifying RNA SARS-CoV-2 vaccine induces potent humoral and cellular immune responses and protects against SARS-CoV-2 variants through T cell-mediated immunity.

Self-amplifying RNA vaccines may induce equivalent or more potent immune responses at lower doses compared to non-replicating mRNA vaccines via amplified antigen expression. In this paper, we demonstrate that 1 µg of an LNP-formulated dual-antigen self-amplifying RNA vaccine (ZIP1642), encoding both the S-RBD and N antigen, elicits considerably higher neutralizing antibody titers against Wuhan-like Beta B.1.351 and Delta B.1.617.2 SARS-CoV-2 variants compared to those of convalescent patients. In addition, ZIP1642 vaccination in mice expanded both S- and N-specific CD3+CD4+ and CD3+CD8+ T cells and caused a Th1 shifted cytokine response. We demonstrate that the induction of such dual antigen-targeted cell-mediated immune response may provide better protection against variants displaying highly mutated Spike proteins, as infectious viral loads of both Wuhan-like and Beta variants were decreased after challenge of ZIP1642 vaccinated hamsters. Supported by these results, we encourage redirecting focus toward the induction of multiple antigen-targeted cell-mediated immunity in addition to neutralizing antibody responses to bypass waning antibody responses and attenuate infectious breakthrough and disease severity of future SARS-CoV-2 variants.

Scattering of e± by C2H6 Molecule over a Wide Range of Energy: A Theoretical Investigation.

The present work reports the theoretical investigation of the scattering of electrons and positrons by the ethane (C2H6) molecule over the energy range 1 eV-1 MeV. The investigation was carried out by taking into account the screening correction arising from a semiclassical analysis of the atomic geometrical overlapping of the scattering observables calculated in the independent atom approximation. The study is presented through the calculations of a broad spectrum of observable quantities, namely differential, integrated elastic, momentum transfer, viscosity, inelastic, grand total, and total ionization cross-sections and the Sherman functions. A comparative study was carried out between scattering observables for electron impact with those for positron impact to exhibit the similarity and dissimilarity arising out of the difference of the collisions of impinging projectiles with the target. Partial-wave decomposition of the scattering states within the Dirac relativistic framework employing a free-atom complex optical model potential was used to calculate the corresponding observable quantities of the constituent atoms. The results, calculated using our recipe, were compared with the experimental and theoretical works available in the literature. The Sherman function for a e±-C2H6 scattering system is presented for the first time in the literature. The addition of the screening correction to the independent atom approximation method was found to substantially reduce the scattering cross-sections, particularly at forward angles for lower incident energies.

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues.

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems' progress.

In-plane directionality control of strongly localized resonant modes of light in disordered arrays of dielectric scatterers.

In this work we propose and analyze techniques of in-plane directionality control of strongly localized resonant modes of light in random arrays of dielectric scatterers. Based on reported diameters and areal densities of epitaxially grown self-organized nanowires, two-dimensional (2D) arrays of dielectric scatterers have been analyzed where randomness is gradually increased along a preferred direction of directionality enhancement. In view of the multiple-scattering mediated wave dynamics and directionality enhancement of light in such arrays, a more conveniently realizable, practical structure is proposed where a 2D periodic array is juxtaposed with a uniform, random scattering medium. Far- and near-field emission characteristics of such arrays show that in spite of the utter lack of periodicity in the disordered regime of the structure, directionality of the high-Q resonant modes is modified such that on average more than 70% of the output power is emitted along the pre-defined direction of preference. Such directionality enhancement and strong localization are nonexistent when the 2D periodic array is replaced with a one-dimensional Bragg reflector, thereby confirming the governing role of in-plane multiple scattering in the process. The techniques presented herein offer novel means of realizing not only directionality tunable edge-emitting random lasers but also numerous other disordered media based photonic structures and systems with higher degrees of control and tunability.

Prospect of biobased antiviral face mask to limit the coronavirus outbreak.

The rapid spread of COVID-19 has led to nationwide lockdowns in many countries. The COVID-19 pandemic has played serious havoc on economic activities throughout the world. Researchers are immensely curious about how to give the best protection to people before a vaccine becomes available. The coronavirus spreads principally through saliva droplets. Thus, it would be a great opportunity if the virus spread could be controlled at an early stage. The face mask can limit virus spread from both inside and outside the mask. This is the first study that has endeavoured to explore the design and fabrication of an antiviral face mask using licorice root extract, which has antimicrobial properties due to glycyrrhetinic acid (GA) and glycyrrhizin (GL). An electrospinning process was utilized to fabricate nanofibrous membrane and virus deactivation mechanisms discussed. The nanofiber mask material was characterized by SEM and airflow rate testing. SEM results indicated that the nanofibers from electrospinning are about 15-30 µm in diameter with random porosity and orientation which have the potential to capture and kill the virus. Theoretical estimation signifies that an 85 L/min rate of airflow through the face mask is possible which ensures good breathability over an extensive range of pressure drops and pore sizes. Finally, it can be concluded that licorice root membrane may be used to produce a biobased face mask to control COVID-19 spread.

Recent Developments in mRNA-Based Protein Supplementation Therapy to Target Lung Diseases.

Protein supplementation therapy using in vitro-transcribed (IVT) mRNA for genetic diseases contains huge potential as a new class of therapy. From the early ages of synthetic mRNA discovery, a great number of studies showed the versatile use of IVT mRNA as a novel approach to supplement faulty or absent protein and also as a vaccine. Many modifications have been made to produce high expressions of mRNA causing less immunogenicity and more stability. Recent advancements in the in vivo lung delivery of mRNA complexed with various carriers encouraged the whole mRNA community to tackle various genetic lung diseases. This review gives a comprehensive overview of cells associated with various lung diseases and recent advancements in mRNA-based protein replacement therapy. This review also covers a brief summary of developments in mRNA modifications and nanocarriers toward clinical translation.

Surface Modification of Graphene Nanoparticles by Acid Treatment and Grinding Process.

Surface modification is necessary to decrease graphene's (GN) stacking process and increase its advantageous properties. In this study, the effects of acid treatment and grinding processes on the structural integrity of GN have been studied. Morphological and structural characteristics of modified GN were investigated by field emission scanning electron microscopy, transmission electron microscopy, gas Pycnometer, particle size analyzer, X-ray diffractometer, UV-Vis spectroscopy and thermal conductivity measurement system which expose some strong evidences of the effects of purification and grinding process on GN nanoparticles in order to get GN based better nanofluid dispersed in water which gives 1.66% and 3.38% enhancement of thermal conductivity at 20 °C and at 40 °C respectively compared to that of DW in this experiment.

Forced Convective Heat Transfer of Aqueous Al2O3 Nanofluid Through Shell and Tube Heat Exchanger.

This study presents the forced convective heat transfer of a nanofluid consisting of distilled water and different weight concentrations (1 wt% and 2 wt%) of Al2O3 nanoparticles flowing in a vertical shell and tube heat exchanger under counter flow and laminar flow regime with certain constant heat flaxes (at 20 °C, 30 °C, 40 °C and 50 °C). The Al2O3 nanoparticles of about 50 nm diameter are used in the present study. Stability of aqueous Al2O3 nanofluids, TEM, thermal conductivity, temperature differences, heat transfer rate, T-Q diagrams, LMTD and convective heat transfer coefficient are investigated experimentally. Experimental results emphasize the substantial enhancement of heat transfer due to the Al2O3 nanoparticles presence in the nanofluid. Heat transfer rate for distilled water and aqueous nanofluids are calculated after getting an efficient setup which shows 19.25% and 35.82% enhancement of heat transfer rate of 1 wt% and 2 wt% aqueous Al2O3 nanofluids as compared to that of distilled water. Finally, the analysis shows that though there are 27.33% and 59.08% enhancement of 1 wt% Al2O3 and 2 wt% Al2O3 respectively as compared to that of distilled water at 30 °C, convective heat transfer coefficient decreases with increasing heat flux of heated fluid in this experimental setup.

Inhibition of Suicidal Erythrocyte Death by Volasertib.

BACKGROUND/AIMS: The Polo-like kinase 1 (Plk1) inhibitor volasertib is used in the treatment of malignancy. Volasertib is partially effective by triggering suicidal death or apoptosis of tumor cells. Similar to apoptosis of nucleated cells, erythrocytes may enter suicidal cell death or eryptosis, which is characterized by cell membrane scrambling with phosphatidylserine translocation to the cell surface and by cell shrinkage. Stimulators of eryptosis include energy depletion, hyperosmotic shock, oxidative stress and excessive increase of cytosolic Ca2+ activity ([Ca2+]i). The present study explored, whether volasertib impacts on eryptosis. METHODS: Human erythrocytes have been exposed to energy depletion (glucose withdrawal for 48 hours), hyperosmotic shock (addition of 550 mM sucrose for 6 hours), oxidative stress (addition of 0.3 mM tert-butylhydroperoxide [tBOOH] for 50 min) or Ca2+ ionophore ionomycin (1 µM for 60 min) in absence and presence of volasertib (0.5-1.5 µg/ml) and flow cytometry was employed to quantify phosphatidylserine exposure at the cell surface from annexin-V-binding, cell volume from forward scatter, [Ca2+]i from Fluo3 fluorescence, reactive oxygen species from 2',7'-dichlorodihydrofluorescein diacetate (DCFDA) fluorescence and ceramide abundance utilizing antibodies. For comparison, annexin-V-binding and forward scatter were determined following a 48 hours exposure of human leukemic K562 cells in RPMI-1640 medium to volasertib. RESULTS: Treatment with volasertib alone did not significantly modify annexin-V-binding or forward scatter in mature erythrocytes. Energy depletion, hyperosmotic shock, oxidative stress and ionomycin, all markedly and significantly increased the percentage of annexin-V-binding erythrocytes, and decreased the forward scatter. Volasertib significantly blunted the effect of energy depletion and hyperosmotic shock, but not of oxidative stress and ionomycin on annexin-V-binding. Volasertib did not significantly influence the effect of any maneuver on forward scatter. In K562 cells, volasertib enhanced annexin-V-binding and decreased the forward scatter. CONCLUSIONS: Volasertib is a novel inhibitor of erythrocyte cell membrane scrambling following energy depletion and hyperosmotic shock, effects contrasting the stimulation of K562 cell apoptosis.

Assessing sustainable waste management practices in Rajshahi City Corporation: an analysis for local government enhancement using IoT, AI, and Android technology.

This study addresses the pressing challenge of inefficient waste management practices within the Rajshahi City Corporation (RCC), Bangladesh. Despite rapid urbanization and escalating waste generation rates, RCC struggles with diverse waste disposal practices, limited supervision, irregular waste collection schedules, and inadequate disposal infrastructure. In this context, this study examines the possible improvements that could be made by combining the Internet of Things (IoT), artificial intelligence (AI), and Android application to improve waste management methods in the RCC. The study's foundation is a vast amount of information gathered from residents, with particular attention paid to waste disposal methods, the role of the local government, the frequency of waste collection, and public attitudes toward waste management. The results point to a complicated waste management environment with a range of waste disposal practices, little supervision, irregular waste collection, and insufficient disposal methods. The importance of RCC in waste management is emphasized, highlighting the need for proactive measures including effective monitoring, constant waste collection, and routine drain cleaning. Additionally, it is suggested that combining IoT, AI, and Android technology is a possible way to improve waste management procedures. These technologies have the potential to increase productivity, lessen their negative effects on the environment, and produce cleaner, more sustainable urban environments.

Clinical Manifestation and Multidisciplinary Treatment Approaches for Primary Tracheal Carcinoma in Bangladesh: A Clinical and Therapeutic Review of 13 Patients.

BACKGROUND: Primary tracheal carcinoma is an exceptionally rare and life-threatening disease that presents significant diagnostic and therapeutic challenges. Delayed diagnosis due to misinterpretation of airway obstruction symptoms often leads to poorer prognoses for patients. This study aimed to explore the clinical manifestations and multidisciplinary treatment approaches for primary tracheal carcinoma in Bangladesh, with a focus on recent advancements in diagnosis and treatment. METHODS: A retrospective observational study was conducted at Bangladesh Specialized Hospital Limited, involving patients aged over 30 who were diagnosed with tracheal carcinoma and underwent multidisiplinary treatment from July 2018 to June 2019. Data were collected through patient interviews and medical record reviews. Descriptive and inferential statistical analyses were performed to examine demographic characteristics, histological variations, tumor locations, clinical signs and symptoms, treatment approaches, and outcomes. RESULTS: The study illuminated varied clinical presentations and the successful application of multidisciplinary approaches among the 13 patients. Invasive squamous cell carcinoma and adenoid cystic carcinoma were the predominant histological subtypes. Symptomatology, including dyspnea, cough, and hemoptysis, highlighted the challenge of early detection. Despite the rarity and intricacies associated with primary tracheal carcinoma, the multidisciplinary strategy yielded generally positive outcomes, as evidenced by a 1-year survival rate of 92.31% and a 5-year survival rate of 76.92%. Kaplan-Meier survival curves underscored the superior efficacy of surgical interventions over non-surgical approaches. CONCLUSION: Despite some limitations, this study contributes crucial insights into the nuanced management of primary tracheal carcinoma in the Bangladeshi context. The demonstrated success of the multidisciplinary strategy, especially surgical interventions, accentuates the importance of definitive resection. The lone case of local recurrence emphasizes the necessity for vigilant follow-up.

Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Detection of Campylobacter spp. in farmed sheep in Mymensingh division of Bangladesh: Prevalence, risk factors, and antimicrobial susceptibility status.

Background and Aim: Campylobacter infections in sheep may be asymptomatic or cause enteritis, ileitis, infertility, and abortion. Thus, this study aimed to estimate the prevalence of Campylobacter spp. in farming sheep and to detect risk factors, molecular patterns, and antimicrobial susceptibility status of these pathogens. Materials and Methods: Four hundred and eight fecal samples were collected from 12 flocks in the Mymensingh and Sherpur districts. Samples were tested by both basic (culture and biochemical tests) and molecular (initially 16S rRNA and later hipO gene-based polymerase chain reaction). Furthermore, the antimicrobial susceptibility status of Campylobacter jejuni was confirmed using disk diffusion. Flock- and animal-level data were captured using semi-structured interviews with farm owners under bivariate and multivariate logistic regression analyses to confirm the risk factors for Campylobacter-positive status. Results: The prevalence of C. jejuni staining at the animal and flock levels was 8.82% (36/408) and 66.70% (8/12), respectively. The age of sheep was identified as an important risk factor. Up to 1 year of age, sheep were 3.78 times more likely to be infected with C. jejuni (95% confidence interval: 1.0736-13.3146, p = 0.038). Of the 36 isolates of C. jejuni, all were found to be fully susceptible (100%) to gentamicin and ciprofloxacin. In this study, three antimicrobial agents, oxytetracycline, azithromycin, and ceftriaxone, were fully resistant (100%). The majority of isolates were resistant to a combination of 4-6 antimicrobial agents. Conclusion: The present study highlights the predominant maintenance of zoonotic Campylobacter species in sheep, and their burden on human health is enormous. Therefore, environmental, animal, and human health needs to be focused under a One Health lens to mitigate the occurrence of Campylobacter in farm settings and to prevent further introduction to animals and humans.

Development of DynamicMC for PHITS Monte Carlo package.

Previously, we have developed DynamicMC for modeling relative movement of Oak Ridge National Laboratory phantom in a radiation field for the Monte Carlo N-Particle package (Health Physics. 2023,124(4):301-309). Using this software, three-dimensional dose distributions in a phantom irradiated by a certain mono-energetic (Mono E) source can be deduced through its graphical user interface. In this study, we extended DynamicMC to be used in combination with the Particle and Heavy Ion Transport code System (PHITS) by providing it with a higher flexibility for dynamic movement for an anthropomorphic phantom. For this purpose, we implemented four new functions into the software, which are (1) to generate not only Mono E sources but also those having an energy spectrum of an arbitrary radioisotope (2) to calculate the absorbed doses for several radiologically important organs (3) to automatically average the calculated absorbed doses along the path of the phantom and (4) to generate user-defined slab shielding materials. The first and third items utilize the PHITS-specific modalities named radioisotope-source and sumtally functions, respectively. The computational cost and complexity can be dramatically reduced with these features. We anticipate that the present work and the developed open-source tools will be in the interest of nuclear radiation physics community for research and teaching purposes.

Assessment of knowledge, attitudes, and practices on vaccine usage among small ruminant farmers in the Northern Region of Bangladesh.

Background and Aim: Small ruminants require vaccines to prevent and manage diseases. Unfortunately, no studies have been conducted in Bangladesh to assess the knowledge, attitudes, and practices (KAP) of small ruminant farmers (SRF) regarding vaccine use against infectious diseases, affecting the success of vaccination campaigns. The present study aims to assess SRF's KAP regarding vaccines, revealing gaps and barriers to efficient vaccination. Materials and Methods: Two hundred and twenty-eight SRF in northern Bangladesh were surveyed in a cross-sectional study. Data were collected from random participants through face-to-face interviews using a structured questionnaire. KAP levels were categorized as "good" or "poor" and "positive" or "negative" using a scoring method with a 60% cutoff. The analysis comprised the utilization of descriptive statistics as well as logistic regression models. Results: Results showed that most participants were female (60.5%), aged 31-40 (34.2%), with secondary education (28.1%), and vaccination training (22.8%). While 75% knew about vaccines, only 37.3% understood their role in preventing infectious diseases, and 63.6% in reducing antibiotic use 68.4% of farmers were aware of negative drawbacks, and 61.8% reported vaccinating their herds. About 42.1% of the farmers had good knowledge, 52.6% had a positive attitude, and 22.8% followed good practices. Female farmers with graduate degrees and 6-10 years of goat farming experience, but not those with vaccination training, demonstrated stronger knowledge. Female farmers with a graduate degree and 6-10 years of goat farming experience displayed positive attitudes. Female goat farmers from Thakurgaon had a higher likelihood of following good vaccination practices than those with vaccination training. Conclusion: The study unearths disparities in KAP scores among farmers. To effectively address KAP gaps concerning vaccine usage and prevent potential infectious diseases, it is essential to design focused educational and training programs. About 52.6% of SRF hold a positive view toward vaccines.

LDSSNV: A Linkage Disequilibrium-Based Method for the Detection of Somatic Single-Nucleotide Variants.

Single nucleotide variants (SNVs) are very common in human genome and pose a significant effect on cellular proliferation and tumorigenesis in various cancers. Somatic variant and germline variant are the two forms of SNVs. They are the major drivers of inherited diseases and acquired tumors respectively. A reasonable analysis of the next generation sequencing data profiles from cancer genomes could provide crucial information for cancer diagnosis and treatment. Accurate detection of SNVs and distinguishing the two forms are still considered challenging tasks in cancer analysis. Herein, we propose a new approach, LDSSNV, to detect somatic SNVs without matched normal samples. LDSSNV predicts SNVs by training the XGboost classifier on a concise combination of features and distinguishes the two forms based on linkage disequilibrium which is a trait between germline mutations. LDSSNV provides two modes to distinguish the somatic variants from germline variants, the single-mode and multiple-mode by respectively using a single tumor sample and multiple tumor samples. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that the LDSSNV method outperforms competing methods and can become a robust and reliable tool for analyzing tumor genome variation.

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review.

Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapist-like assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

Hypoalbuminaemia as a Marker of Severity of Patients of Community Acquired Pneumonia.

Community-acquired pneumonia (CAP) is a common presentation with an acute infection of the pulmonary parenchyma occurring in the community level. Despite the availability of potent antibiotics, it remains as a serious illness with significant morbidity and mortality in both developed and developing countries. This study was undertaken to determine the relation between serum Albumin and severity of CAP. This was a cross sectional descriptive study which was carried out in the Department of Medicine of Mymensingh Medical College Hospital (MMCH), Bangladesh from July 2019 to December 2019. The sample size was 67. Purposive sampling technique was employed. Patients of community acquired pneumonia (CAP), aged ≥14 years of both sex with recently developed radiological pulmonary shadowing with compatible clinical symptoms and signs were included. Patients who were chronically immunosuppressed, with chronic starvation, advanced liver disease or chronic kidney disease with or without receiving haemodialysis were excluded. Data analysis was done by SPSS software for Windows (version 23.0). The mean age 65.7±15.3 years, majority 13(19.4%) patients had chronic lung disease, 12(17.9%) had diabetes mellitus, 9(13.4%) had heart failure, 6(9.0%) had cerebrovascular disease, 6(9.0%) had neoplastic disease and 5(7.5%) had chronic renal failure. Majority 22(32.8%) patients had CURB-65 score 3, out of which 12(54.5%) had albumin level <20g/l, 9(40.9%) had albumin level 20.0-24.9g/l and 1(4.5%) had albumin level 25-29g/l. 17(25.4%) had score 4-5 out of which 10(58.8%) had albumin level <20g/l and 7(41.2%) had albumin level 20.0-24.9g/l, 15(22.4%) had score 2 and 13(19.4%) had score 0-1. Negative significant correction (r=-0.782; p=0.001) was found between CURB-65 score and albumin level. Significant number of patients with severe CAP show low serum albumin level at admission which is statistically significant when compared with CURB-65 score. Thus hypoalbuminaemia may be a good marker of severity of patients with CAP.