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Abstract Background Traditional medicine (TM) interventions are plausible therapeutic alternatives to conventional medical interventions against emerging and endemic zoonotic diseases, particularly in low-and middle-income countries that may lack resources and infrastructure. Despite the growing popularity in the usage of TM interventions, their clinical safety and effectiveness are still contested within conventional healthcare in many countries. Methods We conducted a scoping review of the peer-reviewed literature that synthesises and maps the evidence on TM interventions for the treatment and prevention of zoonoses on the Indian subcontinent. The region, a global hotspot of biodiversity and emerging infections, is characterised by high prevalence of TM use. Based on the scientific literature (mostly case study research, n=l06 studies), our review (1) maps the scope of the literature, (2) synthesises the evidence on the application of TM interventions for zoonoses, and (3) critically reflects on the state of TM and identifies areas for future research focus. Results The evidence synthesis confirmed widespread usage of TM interventions for zoonoses on the subcontinent, with the majority of research reported from India (n=99 studies, 93.4%), followed by Pakistan (n=3 studies, 2.8%), Bangladesh (n=2 studies, 1.9%), and Sri Lanka (n=1, 0.9%). Most of the reviewed studies reported on ethno-medicinal uses of plant species, primarily for treating dengue (n=20 studies), tuberculosis (n=18 studies), Escherichia coli infection (n=16 studies), lymphatic filariasis and cholera (n=9 apiece). However, the evidence on the safety and effectiveness of these reported TM interventions is limited, indicating that these data are rarely collected and/or shared within the peer-reviewed literature. Conclusion This review thus highlights that, whilst TMs are already being used and could offer more widely accessible interventions against emerging and endemic zoonoses and ectoparasites, there is an urgent need for rigorous clinical testing and validation of the safety and effectiveness of these interventions.

The aim of this study was to investigate the structural relationship between teachers' power sources, educational gain, and the mediating role of the psychosocial climate in high school classrooms. Participants included 362 students (223 females and 125 males) who were randomly selected through cluster sampling. All participants voluntarily completed questionnaires about teacher power sources, psychosocial classroom climate, and educational gain. Structural equation modeling and Smart PLS 3.3.2 software were used to analyze the data.The findings indicated a significant positive relationship between teachers' power sources and educational gain. There was also a positive and significant relationship between teachers' power sources and the psychosocial classroom climate. Conversely, a negative relationship was found between the psychosocial classroom climate and educational gain. Moreover, the results showed a positive and significant relationship between teachers' power sources and educational gain, with the psychosocial classroom climate mediating this relationship. These findings emphasize the importance of teachers' optimal use of various power sources to create a favorable psychosocial classroom climate, which can significantly contribute to students' educational gain.

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies.

Introduction: Patient satisfaction is an important indicator of the quality of care given to patients in the health care system. It helps the clinician to know about the exact and objective feedback of the services they provide and to improve the quality and outcome of patient care. Patient satisfaction in physical therapy has not been closely monitored, and limited research exists in the field of health services in this area. The purpose of this study was to measure the patient's satisfaction at the physiotherapy OPD of Hayatabad medical complex Peshawar. Material & Methods: A cross-sectional survey was conducted from October 2018 to January 2019 to measure patient satisfaction. MedRisk patient satisfaction questionnaire by Beattie et al (2005) was used for data collection. The questionnaire was distributed among 125 participants at the physiotherapy OPD of Hayatabad medical complex Peshawar by non-probability convenient sampling. SPSS version 20 was used for data analysis. Results: A total of 125 participants were recruited of which more than half were female (n=70, 56%) and male (n=55,44%) with a mean age of 41.88±12.23. The chief complaint participants presented with was shoulder pain (n=29, 23.2%) followed by low back pain (n=24,19.2%). Patients were satisfied and responded positively to all the MedRisk patient satisfaction questionnaire statements. When asked the participants “My therapist treats me respectfully” (n=4,3.2%) disagree, (n=4,3.2%) neutral responses, and (n=117,93.6%) agree. When inquired about the time therapist spend with participants (n=111, 88.8%) disagree, (n=14, 11.2%) agree. “My therapist thoroughly explains the treatment(s) I receive” (n=3,2.4%) disagree, (n=3,2.4%) neutral response and (n=119,95.2%) agree. “Overall, I am completely satisfied with the services I received from my therapist” (n=1,0.8%) disagree, (n=7,5.6%) neutral response, (n=117,93.6%) agree. No significant correlation was found between gender and patient satisfaction as the p-value was greater than 0.05. Conclusion: This cross-sectional survey showed a high level of patient satisfaction with physiotherapy and good quality care to the patient by a professional therapist in physiotherapy OPD of Hayatabad medical complex Peshawar.

Forest-based communities manage many risks to health and socio-economic welfare including the increasing threat of emerging zoonoses that are expected to disproportionately affect poor and marginalised groups, and further impair their precarious livelihoods, particularly in Low-and-Middle Income (LMIC) settings. Yet, there is a relative dearth of empirical research on the vulnerability and adaptation pathways of poor and marginalised groups facing emerging zoonoses. Drawing on a survey of 229 households and a series of key-informant interviews in the Western Ghats, we examine the factors affecting vulnerability of smallholder and tribal households to Kyasanur Forest Disease (KFD), an often-fatal tick-borne viral haemorrhagic fever endemic in south India. Specifically, we investigate how different socio-demographic and institutional factors interact to shape KFD vulnerability and the strategies employed by households to adapt to disease consequences. Although surveyed households generally perceived KFD as an important health issue in the study region, there was variability in concern about contracting the disease. Overall results showed that poor access to land (AOR = 0.373, 95% CI: 0.152-0.916), being at or below the poverty line (AOR = 0.253, 95% CI: 0.094-0.685) and being headed by an older person (AOR = 1.038, 95% CI: 1.006-1.071) were all significant determinants of perceived KFD vulnerability. Furthermore, KFD vulnerability is also modulated by important extra-household factors including proximity to private hospitals (AOR = 3.281, 95% CI: 1.220-8.820), main roads (AOR = 2.144, 95% CI: 1.215-3.783) and study location (AOR = 0.226, 95% CI: 0.690-0.743). Our findings highlight how homogenous characterisation of smallholder and tribal communities and the 'techno-oriented' approach of existing interventions may further marginalise the most vulnerable and exacerbate existing inequalities. These findings are important for designing context-specific and appropriate health interventions (including the prioritisation of awareness raising, knowledge networks, livelihood diversification) that enhances the resilience of at-risk social groups within the KFD context. More broadly, our findings highlight how a focus on social vulnerability can help national and international health planners improve health interventions and prioritise among diseases with respect to neglected endemic zoonoses.

There is increased global and national attention on the need for effective strategies to control zoonotic diseases. Quick, effective action is, however, hampered by poor evidence-bases and limited coordination between stakeholders from relevant sectors such as public and animal health, wildlife and forestry sectors at different scales, who may not usually work together. The OneHealth approach recognises the value of cross-sectoral evaluation of human, animal and environmental health questions in an integrated, holistic and transdisciplinary manner to reduce disease impacts and/or mitigate risks. Co-production of knowledge is also widely advocated to improve the quality and acceptability of decision-making across sectors and may be particularly important when it comes to zoonoses. This paper brings together OneHealth and knowledge co-production and reflects on lessons learned for future OneHealth co-production processes by describing a process implemented to understand spill-over and identify disease control and mitigation strategies for a zoonotic disease in Southern India (Kyasanur Forest Disease). The co-production process aimed to develop a joint decision-support tool with stakeholders, and we complemented our approach with a simple retrospective theory of change on researcher expectations of the system-level outcomes of the co-production process. Our results highlight that while co-production in OneHealth is a difficult and resource intensive process, requiring regular iterative adjustments and flexibility, the beneficial outcomes justify its adoption. A key future aim should be to improve and evaluate the degree of inter-sectoral collaboration required to achieve the aims of OneHealth. We conclude by providing guidelines based on our experience to help funders and decision-makers support future co-production processes.

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

Smallholder farmer and tribal communities are often characterised as marginalised and highly vulnerable to emerging zoonotic diseases due to their relatively poor access to healthcare, worse-off health outcomes, proximity to sources of disease risks, and their social and livelihood organisation. Yet, access to relevant and timely disease information that could strengthen their adaptive capacity remain challenging and poorly characterised in the empirical literature. This paper addresses this gap by exploring the role of disease information in shaping the adaptive capacity of smallholder farmer and tribal groups to Kyasanur Forest Disease (KFD), a tick-borne viral haemorrhagic fever. We carried out household surveys (n = 229) and in-depth interviews (n = 25) in two affected districts-Shimoga and Wayanad-in the Western Ghats region. Our findings suggest that, despite the generally limited awareness about KFD, access to disease information improved households' propensity to implement adaptation strategies relative to households that had no access to it. Of the variety of adaptation strategies implemented, vaccination, avoiding forest visits, wearing of protective clothing and footwear, application of dimethyl phthalate (DMP) oil and income diversification were identified by respondents as important adaptive measures during the outbreak seasons. Even so, we identified significant differences between individuals in exposure to disease information and its contribution to substantive adaptive action. Households reported several barriers to implement adaptation strategies including, lack of disease information, low efficacy of existing vaccine, distrust, religio-cultural sentiments, and livelihood concerns. We also found that informal information sharing presented a promising avenue from a health extension perspective albeit with trade-offs with potential distortion of the messages through misinformation and/or reporting bias. Altogether, our findings stress the importance of contextualising disease information and implementing interventions in a participatory way that sufficiently addresses the social determinants of health in order to bolster households' adaptive capacity to KFD and other neglected endemic zoonoses.

Endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) is routinely performed for diagnostic evaluation of mediastinal lymphadenopathy due to various etiologies with excellent sensitivity and specificity. Melioidosis can have atypical features like isolated mediastinal lymphadenopathy mimicking as tuberculosis or lymphoma. Differentiation of such atypical melioidosis presentation become difficult due to similar clinical, radiological and even similar EBUS lymph node characteristics. Role of EBUS TBNA in diagnosing melioidosis is under investigated and sparsely reported. We describe two cases of melioidosis diagnosed by point of care rapid lateral flow immunoassay antigen testing and culture of EBUS-TBNA samples from necrotic mediastinal lymph nodes.

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings.

Autism spectrum disorder is a group of disorders marked by difficulties with social skills, repetitive activities, speech, and nonverbal communication. Deficits in paying attention to, and processing, social stimuli are common for children with autism spectrum disorders. It is uncertain whether eye-tracking technologies can assist in establishing an early biomarker of autism based on the children’s atypical visual preference patterns. In this study, we used machine learning methods to test the applicability of eye-tracking data in children to aid in the early screening of autism. We looked into the effectiveness of various machine learning techniques to discover the best model for predicting autism using visualized eye-tracking scan path images. We adopted three traditional machine learning models and a deep neural network classifier to run experimental trials. This study employed a publicly available dataset of 547 graphical eye-tracking scan paths from 328 typically developing and 219 autistic children. We used image augmentation to populate the dataset to prevent the model from overfitting. The deep neural network model outperformed typical machine learning approaches on the populated dataset, with 97% AUC, 93.28% sensitivity, 91.38% specificity, 94.46% NPV, and 90.06% PPV (fivefold cross-validated). The findings strongly suggest that eye-tracking data help clinicians for a quick and reliable autism screening.

On 2 May 2014, a large-scale landslide in Abe Barek, Badakhshan, Afghanistan, produced extensive damage to the buildings and killed hundreds of people. Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present the applicability of a nonlinear geometric correction technique for decreasing the undesired registration errors between pre- and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery, identifying landslide affected areas, and quantifying the landslide volume from DEMs of difference (DoD) analysis. The nonlinear mapping method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the method outperformed the simple DoD technique by eliminating a large-scale of geometric errors in an unaffected area. We estimated the volume of the landslide as 1.05 × 106 m3 from the DoD corrected by the nonlinear method, and discussed the relationship between the area and volume compared to those of the previous studies.

Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies. [ABSTRACT FROM AUTHOR]