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Gallium phosphide is a three-dimensional polymeric material of hetero-diatomic GaP unit. Our studies reveal the synthetic viability of donor base-stabilised monomeric Ga–P compounds with stronger binding energy, and explore their reactivity with metal carbonyls.

Service Oriented Architecture is built on services in which web service discovery is one of the most widely explored domains. Service reputation plays a vital role in the discovery process while selecting the optimal service from a large pool of functionally equivalent services. Reputation mechanism attempts to forecast the future performance of a service based on its past behaviors generally obtained in the form of feedback ratings submitted by users of the service. These feedback ratings about a service may vary from user to user. Variations in feedback ratings could be because of different users’ different subjective judgments and/or dishonest users’ purposely submitted unfair ratings. This paper proposes a service reputation measurement approach counting such diversified act of rating. To realize this goal, an efficient user credibility assessment methodology has been devised by employing the measure of interrater reliability. Experiments are performed to validate the feasibility and effectiveness of the proposed reputation measurement approach. The experimental results depict that the proposed approach can fairly assess service reputations in the presence of various kinds of raters in the system.

Background: Anaemia during pregnancy is a major public health problem worldwide. The prevalence is high in developing countries compared to developed countries. In Bangladesh, anaemia during pregnancy is a leading cause of maternal mortality and morbidity as it is an additional risk factor for pregnancy complications. This study aimed to estimate the prevalence of anaemia among pregnant mothers and determine associated risk factors. Methodology: A cross-sectional study was conducted among pregnant women attending antenatal care (ANC) at Jalalabad Ragib-Rabeya Medical College and Hospital (JRRMCH), Sylhet, from February to May 2022. A total of 400 study participants were selected by systematic random sampling technique. A semi-structured questionnaire was used for data collection. Data were analyzed by using SPSS. A Chi-square test and binary logistic regression test were carried out to determine associated risk factors. A p-value less than 0.05 was considered statistically significant. Result: Among 400 study participants, the prevalence was 53.50%. Among them 32.25% had mild anaemia, 20.25% had moderate anaemia, and 1% had severe anaemia. The prevalence was higher in rural areas (58.09%) than urban areas (46.54%). The majority of them were in the age group between 15-26 years. About 44.75% were primi gravida and 55.25% were multi gravida. About 70.25% of the respondents had taken iron supplementation. Maternal age 27-38 year (Adjusted odd ratio (AOR=1.847)), family monthly income (BDT) 50000-75000 (AOR=2.145), nuclear family (AOR=2.045), para (AOR=2.949), and gravida (AOR=1.636) were found to be independent factors of anaemia among pregnant mothers. Iron and folic acid supplement was protective of anaemia (AOR=0.432). Conclusion: In our study, more than half of pregnant mothers were anaemic and most had mild to moderate anaemia. Based on identified risk factors it is highly recommended to improve socio-economic condition, level of education, family planning services and nutritional status. Iron supplementation should be encouraged for pregnant mothers.

The HAK (High-affinity K+) family members mediate K+ transport that confers normal plant growth and resistance against unfavourable environmental conditions. Rice (Oryza sativa L.) HAK transporters have been extensively investigated for phylogenetic analyses with other plants species with very few of them functionally characterised. But very little information is known about their evolutionary aspects, overall structural, functional characterisation, and global expression pattern of the complete HAK family members in response to salt stress. In this study, 27 rice transporters were phylogenetically clustered with different dicot and monocot family members. Subsequently, the exon–intron structural patterns, conserved motif analyses, evolutionary divergence based different substitution matrix, orthologous-paralogous relationships were studied elaborately. Structural characterisations included a comparative study of secondary and tertiary structure, post-translational modifications, correspondence analyses, normal mode analyses, K+/Na+ binding affinities of each of the OsHAK gene members. Global expression profile under salt stress showed clade-specific expression pattern of the proteins. Additionally, five OsHAK genes were chosen for further expression analyses in root and shoot tissues of two rice varieties during short-term salinity in the presence and absence of exogenous spermidine. All the information can be used as first-hand data for dissecting the administrative role of rice HAK transporters under various abiotic stresses.

Piperaceae is a highly diverse and large family composed of five genera of which Piper and Peperomia are the most abundant. The current study endeavours five Piper species (Piper betle, Piper nigrum, Piper longum, Piper chaba and Piper retrofractum) and one species of Peperomia (Peperomia pellucida) concerning their phytochemical contents, antioxidant and antibacterial properties. Besides their economic uses, these plants also possess curative properties that have been exploited ethnomedicinally since the primaeval days. The methanolic extracts of both fresh (FL) and shade dried (SDL) leaves of these plants showed the presence of various phytochemicals. Among the studied plants, polyphenols like total phenolics (TPC), total flavonoids (TFC) and total tannins (TTC) contents were maximum in P. betle FL extract (TPC:39.50±0.99 mg GAE/ g extract, TFC: 19.40±0.57 mg QE/ g extract and TTC 11.08±0.11 mg GAE/g extract) and significantly higher than the others. Antioxidant efficacies of the extracts by total antioxidant activity (TAA), ferric reducing antioxidant power (FRAP) and ability to scavenge different radicals (DPPH, ABTS, NO and SO), were also highest in P. betle. The study also highlighted the strong antibacterial activities of the extracts against both Gram-positive and Gram-negative bacteria. P. betle FL extract showed the highest activity representing the maximum zone of inhibition (24.65±0.21 mm) and lowest MIC/MBC values (0.58±0.04/0.65±0.07 mg/ml) against E. coli. These findings exhibit the potential of these plant extracts, especially P. betle, in the prospective exploration of plant-derived antioxidants and therapeutic uses of these plants for developing novel antibacterial drugs.

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.

Retrieving mathematical expressions from scientific documents is a challenging task as mathematical expressions or formulae are quite different from the traditional text. Mathematical expressions are highly symbolic and complex. Moreover, the structure of a mathematical formula conveys a semantic meaning which cannot be overlooked. This paper proposes a scientific document retrieval system based on mathematical formula query. The paper explores the concept of Structure Encoded String (SES), which has been employed for mathematical expressions to capture the relations among the formula structures. A pattern based trie indexing scheme has been proposed for faster retrieval. The Jaro-Winkler Similarity has been adopted for matching and ranking. Experiments are conducted, results are reported using standard evaluation measures and compared with similar existing systems.

With the recent advancements in the field of semantic segmentation, an encoderdecoder approach like U-Net are most widely used to solve biomedical image segmentation tasks. To improve upon the existing U-Net, we proposed a novel architecture called Multi-Scale Dilated Fusion Network (MSDFNet). In this work, we have used the pre-trained ResNet50 as the encoder, which had already learned features that can be used by the decoder to generate the binary mask. In addition, we used skip-connections to directly facilitate the transfer of features from the encoder to the decoder. Some of these features are lost due to the depth of the network. The decoder consists of a Multi-Scale Dilated Fusion block, as the main components of the decoder, where we fused the multiscale features and then applied some dilated convolution upon them. We have trained both the U-Net and the proposed architecture on the Ksavir-Instrument dataset, where the proposed architecture has a 3.701 % gain in the F1 score and 4.376 % in the Jaccard. These results show the improvement over the existing U-Net model.

Fluorescent diarylchlorostibane, distibane are utilized as precursors for syntheses of redox-active dimeric/trimeric alkali-metal antimonides by reductive dehalogenation. DFT and EDA-NOCV analysis are performed to shed light on the electron transfer mechanism.

The mode selectivity of methane dissociation is studied on three different Ni/Pt-bimetallic alloy surfaces using a fully quantum approach based on reaction path Hamiltonian. Dissociative sticking probability depends on the composition of alloying metals, excited vibrational mode, and symmetry of the reaction path about the plane perpendicular to the catalyst surface containing the carbon atom and two hydrogen atoms. Our calculations show that symmetry of the minimum energy reaction path depends on the surface alloy composition. In the transition state, the dissociating C-H bond elongates significantly for the dissociation of methane on these alloy systems. A significant decrease in the frequency of the symmetric stretching mode and the two bending modes near the transition state is observed on all the alloy surfaces. Under the vibrational adiabatic limit, excitation of these softened modes enhanced the dissociation probability compared to the ground vibrational state. The reaction probability values decrease abruptly at the incident energies less than the zero-point energy corrected barrier height. With the inclusion of non-adiabatic vibrational coupling terms, reaction probability in the low incident energy region increases to a greater extent, and mode selective behavior also becomes different from that observed within the vibrational adiabatic limit. Symmetric stretching mode displayed the highest reactivity on all the alloy surfaces. Overall, Ni8/Pt(111) is found to be the most reactive toward the methane dissociation.

Decagonal quasiperiodic ordering of Sn thin film on $d$-Al-Ni-Co, is shown based on scanning tunneling microscopy (STM), low-energy electron diffraction and density functional theory (DFT). Interestingly, the decagonal structural correlations are partially retained up to a large film thickness of 10 nm grown at a 165$\pm$10 K. The nucleation centers called 'Sn white flowers' identified by STM at submonolayer thickness are recognized as valid patches of the decagonal clathrate structure with low adsorption energies. Due to the excellent lattice matching (to within 1%) between columns of Sn dodecahedra in the clathrate structure and pentagonal motifs at the $d$-Al-Ni-Co surface, the interfacial energy favors clathrate over the competing Sn crystalline forms. DFT study of the Sn/Al-Ni-Co composite model shows good mechanical stability, as shown by the work of separation of Sn from Al-Ni-Co slab that is comparable to clathrate self-separation energy. The relaxed surface terminations of the R$_2$T$_4$ clathrate approximant are in self-similarity correspondence with the motifs observed in the STM images from monolayer to thickest Sn film., Phys. Rev. B (in press) with 17 figures and 2 Tables in the main text. The supplementary material contains 9 figures, 3 Tables, one note, VASP CONTCAR files, and LEED video files

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods. However, this biometrics domain also has several critical challenges: image rotation, image displacement, change in image scaling, presence of noise in the image due to devices, region of interest (ROI) detection, or user error. For this purpose, a new method of identity verification based on median robust extended local binary pattern (MRELBP) is introduced in this study. In this system, after normalizing the images and extracting the ROI from the microscopic input image, the images enter the feature extraction step with the MRELBP algorithm. Next, these features are reduced by the dimensionality reduction step, and finally, feature vectors are classified using the k-nearest neighbor classifier. The microscopic images used in this study were selected from IITD and CASIA data sets, and the identity verification rate for these two data sets without challenge was 97.2% and 96.6%, respectively. In addition, computed detection rates have been broadly stable against changes such as salt-and-pepper noise up to 0.16, rotation up to 5°, displacement up to 6 pixels, and scale change up to 94%.

Religious involvement in humanitarian aid and development takes countless forms, varying by country, traditions, and sector. Research and operational evaluations have expanded in recent years, with some sectors far better documented than others, notably health and peacebuilding. There are, however, substantial gaps in evidence, most significantly regarding local and non-formal involvement. Pandemic preparedness and response are critical issues ready for further development. Family planning, a USAID priority, exemplifies sensitivities but also positive examples and potential. Experience of religious engagement on women's empowerment including specific issues like gender-based violence (GBV) and child marriage, can be complex, but their importance warrants particular attention. Child protection poses similar issues. [ FROM AUTHOR] Copyright of Review of Faith & International Affairs is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

This essay introduces a special, open-access issue of this journal focused on the intersection of religion and relief & development. It describes the discussions—of terminology and tangibility, of ...

Introduction Pyogenic granulomas are benign vascular lesions of the skin and mucosa which are often a source of concern because of their recurrent bleeding even with minimal trauma. Current treatment for pyogenic granuloma is ablative; no medical therapy is standardized to date. Timolol, due to its vasoconstrictive effect, vascular growth factor inhibition and apoptosis promotion properties, is a potential therapeutic option. Objectives: To assess the effectiveness and safety of topical timolol in the treatment of pyogenic granulomas. Methods A two-centre, double-blind and placebo-controlled trial (Registration CTRI/2019/04/018581) was conducted. Patients of either sex were recruited with pyogenic granuloma lesions of less than eight weeks duration. Topical treatment with 0.5% timolol or matching glycerin placebo was continued for six weeks. Changes in color, size, bleeding tendency, physicians’ and patients’ global assessments and adverse events were assessed. Results Forty subjects were randomized between the two groups which were comparable in age, sex, duration of illness and baseline lesion size.Significant improvement was noted with timolol, with color change from first follow-up onwards and lesion size reduction from second follow-up onward. Patients’ assessment of bleeding tendency also showed imrovement from the second visit onward. Between-group comparison showed significant difference with respect to percentage reduction in size (timolol 40.9%, placebo 3.4%; P = 0.002). Rescue treatment (electrosurgery) was required in five patients on placebo and in one in the timolol group (P = 0.182). Complete resolution occurred in 2 (10%) patients with timolol and in no patients on placebo (P = 0.231). Limitations: We observed effects of treatment for only six weeks. Conclusion Topical timolol may be a treatment option for early pyogenic granulomas but complete resolution is unlikely in six weeks. Studies of longer duration are required to assess resolution and recurrence rates.

Objectives: This study reviews faculty members’ comfort level with remote teaching in the Fall 2020 semester to evaluate the effectiveness of the professional development workshops. Method: Using survey research, we examined professional development activities and subsequent comfort level and ease of adjustment with remote teaching in Fall 2020. Results: Following the training, faculty reported high planned usage of various online teaching tools and great comfort with using them. The data reveals some differences between part-time and full-time faculty members. Conclusions: The experience gained in the emergency semester, combined with the targeted professional development workshops offered eased the stress of planned remote teaching in the following semester. Implication for Theory and/or Practice: For institutions planning to incorporate educational technology in the future, the implementation and evaluation of the targeted workshops may serve as a replicable model. It may also help institutions be better prepared for emergency remote teaching in the future.

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.

The dynamics of sexually transmitted infections (STIs) are influenced by sexual behaviour (age of first sexual exposure, number of sexual partner/s, sexual orientation, mode of intercourse, addiction, occupation, correct and consistent use of condom). The presence of the human immunodeficiency virus (HIV) influences the clinical pattern of STIs and the course of disease too.Our study was conducted to find the prevalence of STIs among attendees of a designated STI/RTI clinic (DSRC) with the description of clinico-demographic profile and sexual behaviour and association of HIV seropositivity with STIs.Ours was an institution-based cross-sectional study conducted at DSRC after obtaining ethics committee approval and consent from patients. Clinical diagnosis, demographic profile and sexual history were obtained after maintaining audio-visual privacy.Medcalc, version 10.2 was used.Among 167 patients attending DSRC during the study period, 76 having sexual dysfunctions were excluded. The major STIs diagnosed were primary syphilis (6.59%), secondary syphilis (2.2%), herpes genitalis (42.86%), condyloma acuminata (26.37%), molluscum contagiosum (5.49%), genital candidiasis (7.69%), bowenoid papulosis (3.3%), genital scabies (4.4%) and chancroid (1.1%). Sexual orientations were heterosexual (64.83%), lesbian (1.1%), gay (12.1%), bisexual (17.58%) and with an animal partner (4.4%). Among the total study population, 8.8% was rapid plasma reagin (RPR) reactive and 12.1% was Integrated Counselling and Testing Centre (ICTC) reactive. Among the total patients who used condoms (Sexual behaviour influences the disease outcome in patients with reproductive tract infection (RTI)/STI.

Marfan syndrome (MFS) is a rare inherited disorder of connective tissue characterized by various phenotypical and genetic manifestations. It involves mutation in FBN1 gene which encodes for microfibrillin glycoprotein fibrillin-1. Predominant involvement occurs in eyes, skeleton and cardiovascular system. Cardiovascular abnormalities such as aortic root dilatation and mitral valve prolapse are the two main life-threatening complications associated with MFS. The diagnosis is based upon clinical findings, some of which are age dependent. If not properly treated, premature death may be caused by the severe cardiovascular and pulmonary complications associated with Marfan syndrome. Therefore, it is important to identify this potentially life-threatening condition in general practice. We are presenting two cases of Marfan syndrome from same family due to its rarity. BANGLADESH J CHILD HEALTH 2021; VOL 45 (2) : 108-110

Background: Leprosy is an infectious disease which faces diagnostic challenges. Slit-skin smear (SSS) is an age simple diagnostic technique, yet not commonly applied by health care providers. The study aimed to determine the effectiveness of SSS in terms of validity, diagnostic accuracy, and percentage agreement against punch biopsy in diagnosing leprosy among leprosy patients who were diagnosed with leprosy on clinical grounds only (i.e., number of skin lesions and/or peripheral nerve thickening). Methods: An evaluation study of diagnostic tests with a cross-sectional design was conducted at a tertiary care center of Bankura. In general, 70 new untreated leprosy patients, diagnosed solely by clinical grounds (i.e., count of skin lesions and/or thickening of the nerve) and attending the dermatology outpatient department (From February 2019 to January 2020) were enrolled in this study. After excluding pure neuritic, relapse, and seriously ill patients by consecutive sampling, they were subjected to both SSS and punch biopsy using a standard process. SPSS for Windows (Version 16.0., Chicago, SPSS Inc.) was used to analyze data. Z test, chi-square test, and kappa test were conducted to test the statistical significance between the effectiveness of SSS and punch biopsy. Results: The sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and diagnostic accuracy of SSS were 81.81%, 95.83%, 90%, 92%, 20%, 0.19%, 102.87%, and 91.42%, respectively. Based on the results, SSS could detect acid-fast bacilli (AFB) in clinically diagnosed leprosy cases slightly less than punch biopsy, but it was statistically insignificant (Z=0.3689, P=0.71138, df=1). Finally, Cohen’s Kappa coefficient was 0.796, representing substantial agreement between SSS and punch biopsy (95% CI: 0.641-0.951). Conclusions: Overall, SSS is more or less equally effective as compared to punch biopsy in confirming leprosy cases. Interest and training on SSS among resident doctors should be emphasized accordingly.

Electrochemical forming (or electroforming), is gaining recognition as an additive and sustainable manufacturing process. Electroforming could shape part of the fourth industrial revolution through thoughtful and systematic modelling. For this to be useful, efficient simulation of the electroforming process in the 4.0 era should be used as a tool to design, test and propose optimised processes. Emphasis should be put on building models that provide new information about the electrochemical systems under investigation or lay the groundwork for innovative applications and provide the industry with options to update obsolete process models currently in use. This will lead to a decrease in process waste, lower inventory needs, perhaps meet environmental regulations and optimise supply chains. In this review article, key studies on modelling electroforming processes are critically reviewed against this framework. The authors suggest some useful modelling approaches using commercial software.

Leafy vegetables are important for their nutritive and medicinal values. However, many of them are less explored and underutilized. The present study evaluates the antioxidant and antibacterial activities present in five underutilized leafy vegetables such as Alternanthera philoxeroides, Boerhavia diffusa, Enydra(=Enhydra) fluctuans, Glinus oppositifolius and Suaeda maritima. Methanolic extracts from leafy edible parts of these plants possessed diverse phytochemicals. Of these plants, the polyphenol contents like total phenolic (TPC), total flavonoid (TFC) and total tannin (TTC) were the highest in E. fluctuans with significant variations concerning other plants. Also, the antioxidant activities of E. fluctuans were greater comparing other plants; the parameters were the total antioxidant activity (TAA), ferric reducing power assay and ability to scavenge DPPH, ABTS and superoxide radicals. The effect of varying extract-quantities revealed the highest antibacterial activity of the E. fluctuans extracts with substantial inhibition zones against six bacterial strains. Accordingly, the E. fluctuans extracts showed the lowest MIC (2.75±0.35 mg ml-1) and MBC (3.50±0.71 mg ml-1), albeit with significant variations with ampicillin. The study highlights that these plants are the untapped repertoires of natural antioxidants and antimicrobials for dietary and therapeutic uses.

Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.

Leprosy is a disease having tremendous social implications due to ostracization. Despite continuous efforts made so far to eliminate leprosy, stigma/misbeliefs/adverse attitude toward leprosy still prevails among common people. Community perceptions and attitudes towards leprosy patients are critical and unique indicator of how society stereotypes leprosy.The qualitative study was conducted to explore the perceptions of leprosy patients towards leprosy.Two focused group discussions (FGDs) with 19 newly-diagnosed leprosy patients who can speak vernacular were conducted with help of a predetermined FGD-guide between April and June 2019 in the dermatology out-patient department of the tertiary-care center. Both FGD sessions were recorded, transcribed, and translated into English. These verbatims were thematically analyzed and emerging themes were identified with illustrative quotations. Free list and pile sort data obtained were analyzed by Visual Anthropac version 1.0 software for Smith's salience value Cognitive mapping with two-dimensional scaling and hierarchical cluster analysis.Small size of wound, not giving importance, failure to realize, bad luck and scared of being isolated etc., were the most common reported perceptions. Overall, five themes emerged after pile sorting, namely ignorance about the disease, stigma and economic burden, positive perception after diagnosis, treatment-related suggestions, and myths and frequently asked questions.The study concludes that more IEC campaigns are required to abolish the stigma; and ASHA workers/front-line workers can play a pivotal role. The program (NLEP) can also utilize the role of satisfied cured-patient as peer-educator to improve the attitude of society towards this disease.