Chandipura Virus Resurgence: Insights from Indian Outbreaks and the Path Forward.

BACKGROUND: First isolated in 1965 from a case of febrile encephalopathy, the Chandipura virus (CHPV) causes sporadic cases as well as periodic outbreaks of encephalitis in parts of India. Transmitted by sandflies and mosquitoes, CHPV infection has high mortality within 48 hours of hospitalisation, with children bearing the brunt of the illness. The virus garnered global attention in the middle of 2024 as India witnessed its largest outbreak in the last two decades. OBJECTIVE: This article aims to synthesise the existing knowledge on various aspects of CHPV and outline current actions needed as well as potential directions for future research. CONTENT: Between early June and August 15th 2024, India reported 245 cases of encephalitis from the states of Gujarat and Rajasthan, 64 of which were laboratory-confirmed CHPV infections. The mortality toll of the outbreak was 82, accounting for a case fatality rate of 33%. With this outbreak, the virus has expanded its niche from central and southern to north-western India. Significant advancements in the understanding of the neuropathogenesis of the virus and the development of diagnostic assays have been made in the 21st century. However, no specific antiviral drugs or vaccines are available. A G-protein-based recombinant vaccine and an inactivated vaccine have shown favourable results in pre-clinical trials. The need of the hour is to fast-track the development of an effective vaccine. A high suspicion for early identification and prompt referral of cases, decentralized diagnostic facilities, sensitization of healthcare workers, integrated vector management and effective reporting and surveillance systems are all needed to curb the menace of this perilous pathogen. The current outbreak should serve as a wake-up call to foster intersectoral collaboration between policymakers, public health experts, epidemiologists, virologists, neurologists, paediatricians, and anthropologists to develop and implement effective strategies against the virus.

Autonomic rehabilitation: Vagal and sympathetic impacts of modified occipitomastoid suture V-spread.

BACKGROUND: Autonomic rehabilitation using osteopathic manipulative treatment (OMT) to stimulate the vagus nerve may be a good adjunct therapy by physiatrists who treat dysautonomia. Heart rate variability (HRV) may provide quantitative evidence for the benefits of OMT on autonomic nervous system function. Elevated HRV indicates optimal health and reduced mortality risk, whereas low HRV is associated with stress, pain, and chronic disease pathology. OBJECTIVE: To analyze the impact of modified occipitomastoid suture v-spread OMT technique on vagal tone by measuring HRV. DESIGN: Within-participant design with 5-minute HRV measurement pre- and post-OMT treatment. SETTING: Clinical examination suites. PARTICIPANTS: Thirty healthy osteopathic medical students. INTERVENTIONS: A modified occipitomastoid suture v-spread treating somatic dysfunction by osteopathic physicians trained in osteopathic neuromusculoskeletal medicine. MAIN OUTCOME MEASURES: HRV variables related to vagal tone including root mean square of successive differences (RMSSD), percentage of successive normal sinus RR intervals >50 ms (pNN50), high frequency (HF), Parasympathetic Nervous System Index (PNSI), and Sympathetic Nervous System Index (SNSI). Repeated measures t-test analyzed the difference in mean HRV values after OMT. RESULTS: There were statistically significant increases in each of the HRV measures after OMT. Participants had a mean (95% confidence interval) RMSSD of 50.5 ms (38.3-62.8) at baseline and 55.0 ms (41.2-68.7, p = .013) post-OMT. pNN50 was 24.5% (17.6-31.3, n = 30) at baseline and 28.2% (20.8-35.6, p = .003) post-OMT. HF was 1549.6 ms2 (389.8-2709.4) at baseline and 1901.8 ms2 (618.2-3185.4, p = .103) post-OMT. PNSI was -0.1 (-0.5 to 0.2) at baseline and 0.1 (-0.3 to 1.5, p < .01) post-OMT, and SNSI was 0.2 (-0.1 to 0.6) at baseline and 0.05 (-0.3 to 0.4, p < .01) post-OMT. Normalization, due to high baseline variance, results include RMSSD 1.1 ms (1.0 to 1.1, p = .012), pNN50 1.3 (1.1 to 1.4, p = .011), and HF 1.2 ms2 (1.1 to 1.4, p = .018). CONCLUSIONS: Modified occipitomastoid suture v-spread may provide clinical benefit through increased vagal tone and decreased sympathetic activity.

Shortening Dome Osteotomy for the Correction of Coronal Plane Elbow Deformities.

Background: Severe elbow deformities are common in developing countries because of neglect or as a result of prior treatment that achieved poor reduction. Various osteotomy techniques have been defined for the surgical correction of elbow deformities1-9. However, severe elbow deformities (>30°) pose a substantial challenge for surgeons because limited surgical options with high complication rates have been described in the literature. Shortening dome osteotomy is a useful method of correcting moderate-to-severe deformities and offers all of the advantages of previously described dome osteotomy without causing an undue stretching of neurovascular structures8,9. Description: The anesthetized patient is placed in a lateral decubitus position under tourniquet control with the operative limb up, the elbow in 90° of flexion, and the forearm draped free to hang over a bolster kept between the chest and the forearm. A posterior midline approach is utilized, with the incision extending from 6 cm proximal to the tip of the olecranon to 2 cm distal. The ulnar nerve is identified and protected during the entire surgical procedure. In case of severe (>30°) and long-standing cubitus varus deformity, anterior transposition of the ulnar nerve is additionally performed to prevent nerve stretching after the deformity correction. A midline triceps-splitting approach is utilized along with subperiosteal dissection to expose the metaphyseodiaphyseal region of the distal humerus. Alternatively, the operating surgeon may choose to utilize a triceps-sparing approach. Hohmann retractors are placed at the medial and lateral aspects of distal humerus to protect the anterior neurovascular structures. Careful extraperiosteal dissection and a transverse incision over the anterior periosteum are performed to facilitate rotation of the distal fragment, as the anterior periosteum is usually thickened in cases of long-standing deformities. The posterior midline axis of the humerus is marked on the skin. The dome of the olecranon fossa is identified, and the distal osteotomy line is made just proximal and almost parallel to the dome. The proximal osteotomy line is made parallel and 5 to 8 mm proximal to the distal osteotomy line, as any further larger shortening may affect the muscle length-tension relationship. The posterior cortices of both domes and of the medial and lateral supracondylar ridges are osteotomized with use of an ultrasonic bone scalpel (Misonix), which was set at 70% amplitude control and 80% irrigation control. Alternatively, the osteotomy may be made by making multiple drill holes and connecting them with a 5-mm sharp osteotome or with use of a small-blade oscillating saw. The osteotomy of the anterior cortex is completed under direct vision with use of a Kerrison upcutting rongeur, after the subperiosteal separation of bone in order to protect the surrounding soft tissues. Kirschner wires are inserted in the distal fragment, and can be used like joysticks to manipulate the distal fragment to facilitate correction. Often, the anterior periosteum is found thickened and resists the free rotation of the distal fragment. In that case, a careful anterior extraperiosteal dissection is performed to protect the neurovascular structures, and this thickened periosteum may need to be incised transversely to facilitate deformity correction. Correction is achieved by rotating the distal fragment about the proximal fragment along the line of the parallel dome cuts, as per the preoperative planning, and correction may be verified intraoperatively using an image intensifier. Once the desired correction is obtained, the osteotomy site is provisionally fixed with Kirschner wires. Internal fixation is achieved with the help of a locking reconstruction or anatomically contoured posterolateral distal humerus locking plate plate applied over the posterolateral aspect of the distal humerus. Alternatively, the osteotomy site may be fixed with use of crossed-column screws. The osteotomy site may be grafted with small bone chips harvested from the excised curved bone fragment. Alternatives: Closing-wedge osteotomy is a simple technique for deformity correction. However, this procedure requires removal of a large wedge1 in cases of severe deformities, which leads to the generation of high displacing forces at the osteotomy site and at the prominence of the lateral condyle, as well as associated stretching of the ulnar nerve in cubitus varus correction. Other osteotomies like a step-cut osteotomy3, pentalateral osteotomy4, and 3D osteotomy5 are viable options for severe deformities; however, these techniques are difficult to reproduce because of their complex intraoperative templating, poor precision, and difficulty in maintaining fixation for higher degrees of deformity. Conventional dome osteotomy is a simple and reproducible method for the correction of severe deformities, but is associated with a large valgus moment and nerve stretching2. Shortening dome osteotomy offers all the advantages of conventional dome osteotomy, along with the added benefit of decreased tension in the neurovascular bundle8,9. Rationale: Removal of a concentric curved piece of the bone enables the surgeon to correct even a severe deformity with greater ease and precision, without causing any undue stretching of the ulnar nerve. The surface area of the proximal dome (concave) is less than that of distal dome (convex) because of the natural distal humeral flare. The deformity correction involves additional medial translation of the distal fragment that prevents lateral condylar prominence. Expected Outcomes: In a study of 18 patients with a mean age of 7.5 years (range, 5 years to 11 years), Singh et al.8 reported that the mean radiographic ulnohumeral angle improved from 26.1° varus (range, 22° to 34°) preoperatively to 7.3° valgus (range, 2° to 12°) postoperatively (p < 0.001). The mean lateral condylar prominence index was -2.4° (range, +4.7° to -10.5°) preoperatively compared with -1.7° (range, +4.5° to -5.1°) postoperatively (p = 0.595). Radiographic healing was observed in all of the patients at a mean of 7.1 weeks (range, 5 to 9 weeks). All patients regained their preoperative range of elbow motion within 6 months postoperatively. Important Tips: The dome of the olecranon fossa is identified, and the distal osteotomy line is made just proximal and parallel to the dome. The proximal osteotomy line is made parallel and 5 to 8 mm proximal to the distal osteotomy line.The posterior midline axis of the humerus is marked on the skin, as measuring displacement at this mark will help assess the magnitude of correction achieved.The posterior cortices of both of the domes are osteotomized with use of an ultrasonic bone scalpel. The osteotomy of the anterior cortex is completed under direct vision with use of a Kerrison upcutting rongeur, after the subperiosteal separation of bone in order to protect the surrounding soft tissues.Careful extraperiosteal dissection and a transverse incision over the anterior periosteum are performed to facilitate rotation of the distal fragment, as the anterior periosteum is usually thickened in cases of long-standing deformities. Acronyms and Abbreviations: K-wire = Kirschner wire.

Characterization and assessment of exposure risks of healthcare personnel during the COVID-19 pandemic.

Introduction: Healthcare personnel have the right to decent, healthy, and safe working conditions during COVID-19. Despite our best efforts to safeguard them against SARS-CoV-2 infection, a substantially large number of healthcare personnel fell ill and succumbed to COVID-19. This study aimed to characterize the SARS-CoV-2 exposures among healthcare personnel and determine the risk level of those events, thereby identifying and focusing on the key areas that influence workplace safety. Materials and Methods: A pre-tested questionnaire was used to collect data from healthcare personnel on the type of exposure, place, and use of preventive measures, along with demographic and occupational data. Exposure events were categorized into low, moderate, and high-risk by using a risk categorization protocol. Results: Doctors were primarily found to be involved in the aerosol-generating procedure, which had 94 times higher odds (95% CI: 43.9926-201.17) for high-risk exposures than other activities. In contrast, nurses were more likely to experience close-contact exposures (OR: 2.77, 95% CI: 1.44-5.33). Both critical care units and operation theaters were identified to have higher odds (OR: 2.34, 95% CI: 1.33-4.23 and OR: 2.31; 95% CI: 0.99-5.42, respectively) than the wards for high-risk exposures. Use of personal protective equipment was poor, with breaches reported in 10.2% of all exposure events. The lacunae in practice were addressed by repeated training and counseling and by ensuring the adequacy of resources in required areas. Conclusions: The assessment was beneficial for safeguarding healthcare personnel and assuring a safer workplace during the early phase of the COVID-19 pandemic.

Exploring the functionality of fluorescent liposomes in cancer: diagnosis and therapy.

Fluorescent liposomes are pivotal in cancer research, serving as adaptable vehicles for imaging and therapeutics. These small lipid vesicles, capable of encapsulating fluorescent dyes, offer precise visualization and monitoring of their targeted delivery to cancer cells. This review delves into the critical role fluorescent liposomes play in enhancing both cancer diagnosis and treatment. It provides an in-depth analysis of their structural features, fluorescent labeling techniques, targeting strategies, and the challenges and opportunities they present. In the domain of cancer diagnosis, the article sheds light on various imaging modalities enabled by fluorescent liposomes, including fluorescence imaging and multimodal techniques. Emphasis is placed on early detection strategies, exhibiting the utility of targeted contrast agents and biomarker recognition for enhanced diagnostic precision. Moving on to cancer treatment, the review discusses the sophisticated drug delivery mechanisms facilitated by fluorescent liposomes, focusing on chemotherapy and photodynamic therapy. Moreover, the exploration extends to targeted therapy, explaining the applications of fluorescent liposomes in gene delivery and RNA interference. In a nutshell, his article comprehensively explores the multifaceted impact of fluorescent liposomes on advancing cancer diagnosis and treatment, combining existing knowledge with emerging trends.

Predicting hospital length of stay using machine learning on a large open health dataset.

BACKGROUND: Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient's length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper. METHODS: We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns. RESULTS: The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns. CONCLUSION: Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Advancing psoriasis drug delivery through topical liposomes.

Psoriasis, recognized as a chronic inflammatory skin disorder, disrupts immune system functionality. Global estimates by the World Psoriasis Day consortium indicate its impact on approximately 130 million people, constituting 4 to 5 percent of the worldwide population. Conventional drug delivery systems, mainly designed to alleviate psoriasis symptoms, fall short in achieving targeted action and optimal bioavailability due to inherent challenges such as the drug's brief half-life, instability, and a deficiency in ensuring both safety and efficacy. Liposomes, employed in drug delivery systems, emerge as highly promising carriers for augmenting the therapeutic efficacy of topically applied drugs. These small unilamellar vesicles demonstrate enhanced penetration capabilities, facilitating drug delivery through the stratum corneum layer of skin. This comprehensive review article illuminates diverse facets of liposomes as a promising drug delivery system to treat psoriasis. Addressing various aspects such as formulation strategies, encapsulation techniques, and targeted delivery, the review underscores the potential of liposomes in enhancing the efficacy and specificity of psoriasis treatments.

Functional connectivity changes in meditators and novices during yoga nidra practice.

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines.

Consistent movement of viewers' facial keypoints while watching emotionally evocative videos.

Neuropsychological research aims to unravel how diverse individuals' brains exhibit similar functionality when exposed to the same stimuli. The evocation of consistent responses when different subjects watch the same emotionally evocative stimulus has been observed through modalities like fMRI, EEG, physiological signals and facial expressions. We refer to the quantification of these shared consistent signals across subjects at each time instant across the temporal dimension as Consistent Response Measurement (CRM). CRM is widely explored through fMRI, occasionally with EEG, physiological signals and facial expressions using metrics like Inter-Subject Correlation (ISC). However, fMRI tools are expensive and constrained, while EEG and physiological signals are prone to facial artifacts and environmental conditions (such as temperature, humidity, and health condition of subjects). In this research, facial expression videos are used as a cost-effective and flexible alternative for CRM, minimally affected by external conditions. By employing computer vision-based automated facial keypoint tracking, a new metric similar to ISC, called the Average t-statistic, is introduced. Unlike existing facial expression-based methodologies that measure CRM of secondary indicators like inferred emotions, keypoint, and ICA-based features, the Average t-statistic is closely associated with the direct measurement of consistent facial muscle movement using the Facial Action Coding System (FACS). This is evidenced in DISFA dataset where the time-series of Average t-statistic has a high correlation (R2 = 0.78) with a metric called AU consistency, which directly measures facial muscle movement through FACS coding of video frames. The simplicity of recording facial expressions with the automated Average t-statistic expands the applications of CRM such as measuring engagement in online learning, customer interactions, etc., and diagnosing outliers in healthcare conditions like stroke, autism, depression, etc. To promote further research, we have made the code repository publicly available.

Pectin hydrogels for controlled drug release: Recent developments and future prospects.

Pectin hydrogels have emerged as a highly promising medium for the controlled release of pharmaceuticals in the dynamic field of drug delivery. The present review sheds light on the broad range of applications and potential of pectin-based hydrogels in pharmaceutical formulations. Pectin, as a biopolymer, is a versatile candidate for various drug delivery systems because of its wide range of properties and characteristics. The information provided on formulation strategies and crosslinking techniques provides researchers with tools to improve drug entrapment and controlled release. Furthermore, this review provides a more in-depth understanding of the complex factors influencing drug release from pectin hydrogels, such as the impact of environmental conditions and drug-specific characteristics. Pectin hydrogels demonstrate adaptability across diverse domains, ranging from applications in oral and transdermal drug delivery to contributions in wound healing, tissue engineering, and ongoing clinical trials. While standardization and regulatory compliance remain significant challenges, the future of pectin hydrogels appears to be bright, opening up new possibilities for advanced drug delivery systems.

Prospective comparative evaluation of intraoperative joint stability and component placement between posterior approach to hip and its quadriceps coxae sparing modification for primary hip arthroplasty.

Background: Efforts at minimizing the propensity of posterior approach of hip for post-operative dislocation after total hip arthroplasty have been a subject of continual research. We comparatively evaluated the posterior approach to hip and its quadriceps coxae sparing (QCS) modification with regard to joint stability and component placement.Questions/Purposes: (1) Does sparing of Quadriceps Coxae tendons vis a vis their sectioning in posterior approach help in achieving better intraoperative stability? (2) Does sparing of Quadriceps Coxae tendons come in the way of adequate surgical exposure sufficient to place hip components accurately? (3) Does sparing of Quadriceps Coxae tendons result in better early functional outcome? Methods: Seventy-two patients requiring THA were enrolled prospectively and randomized into two groups: group A (posterior approach) and group B quadriceps coxae sparing (QCS) approach. They were compared for intraoperative joint stability, accuracy of component placement, duration of surgery, blood loss, any event of hip dislocation, and functional outcome. Results: QCS approach resulted in better intraoperative hip stability as the hips tolerated a higher value of 43.50±3.5° mean internal rotation (at 90° of flexion and 20° of adduction) in this group versus a mean value of 33.50±5.5° in posterior approach group (p < 0.01). No significant difference was observed for femoral version, acetabular inclination, and acetabular version in the two groups (p > 0.05). There was no difference between the two groups in terms of duration of surgery, intraoperative blood loss, and functional outcome at one year (p > 0.05). No event of hip dislocation was observed in QCS approach, whereas, one such event happened in the posterior-approach group. Conclusion: Preservation of the QC tendons improves the intraoperative stability of THA and they do not cause any hindrance in the exposure required for optimum component placement.

The impact of civil commitment laws for substance use disorder on opioid overdose deaths.

Objective: Our study analyzed the impact of civil commitment (CC) laws for substance use disorder (SUD) on opioid overdose death rates (OODR) in the U.S. from 2010-21. Methods: We used a retrospective study design using the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset to analyze overdose death rates from any opioid during 2010-21 using ICD-10 codes. We used t-tests and two-way ANOVA to compare the OODR between the U.S. states with the law as compared to those without by using GraphPad Prism 10.0. Results: We found no significant difference in the annual mean age-adjusted OODR from 2010-21 between U.S. states with and without CC SUD laws. During the pre-COVID era (2010-19), the presence or absence of CC SUD law had no difference in age-adjusted OODR. However, in the post-COVID era (2020-21), there was a significant increase in OODR in states with a CC SUD law compared to states without the law (p = 0.032). We also found that OODR increased at a faster rate post-COVID among both the states with CC SUD laws (p < 0.001) and the states without the law (p = 0.019). Conclusion: We found higher age-adjusted OODR in states with a CC SUD law which could be due to the laws being enacted in response to the opioid crisis or physicians' opposition to or unawareness of the law's existence leading to underutilization. Recent enactment of CC SUD law(s), a lack of a central database for recording relapse rates, and disparities in opioid overdose rate reductions uncovers multiple variables potentially influencing OODR. Thus, further investigation is needed to analyze the factors influencing OODRs and long-term effects of the CC SUD laws.

Linking Generalized Ligamentous Laxity to Musculoskeletal Injury: A Study in the Indian Population.

Introduction Generalized ligamentous laxity (GLL) is defined as an increased range of motion across multiple joints in an individual beyond the mean range of motion in the general population, with a reported prevalence between 5% and 15%. It becomes less common with age and is more common in females and in the lower limbs. Musculoskeletal injury (MSI) is damage to musculoskeletal systems, usually due to strenuous activity. There is conflicting literature regarding whether the risk of MSI during strenuous activity is higher in individuals with GLL and a dearth of evidence from the Indian subcontinent regarding GLL. This study determines if GLL predisposes to musculoskeletal injuries among patients. Materials and methods One hundred eighty patients each were selected as cases and controls after obtaining informed consent, a Beighton score assessment, and a questionnaire regarding injury-filled in all participants with GLL. Result Thirty-three participants (18.33%) in the case group and 16 participants (8.89%) in the control group were found to have GLL. An odds ratio of 2.30 (using a 2x2 RC table) was calculated between participants with GLL among the cases and controls with a Beighton score of 4/9, and a significantly higher mean Beighton score (p=0.018) was found among participants presenting with MSI (cases) than participants without MSI (controls). Discussion The study found that there was a significant prevalence of GLL in the adult population, especially in females compared to males. The younger age group was also comparatively much more involved. It also proved that GLL was more common in patients with MSI and that hyper-mobile people had a twofold chance of injury compared to the general population. The joints of the lower limb were more frequently involved, probably the weight-bearing joints, the most common being the ankle and knee. People with GLL also had higher chances of repeating injuries. Conclusion This study has implications for the prevention of injuries in people with GLL. Screening such individuals to identify those with GLL using the Beighton score could be beneficial. Though orthopedic surgeons primarily manage people with MSI, they rarely identify individuals with GLL, and making a diagnosis regarding the same definitely helps these individuals live pain-free lives.

Drivers' liability-based comparative severity analysis of motorized two-wheeler crashes.

OBJECTIVE: This study aims to investigate comparative severity analysis of motorized two-wheeler (MTW) crashes based on drivers' liability using police-reported a crash data base. METHODS: Using crash data from 2016 to 2020, this study examines and analyses the key factors affecting the severity of MTW injuries in Dehradun. For analysis, the ordinal logistic approach is used because severity levels are attributed with natural ordering. Differentiating from past studies, this research distinguished between collisions in which MTW crashes were considered as first party (crash in which MTW rider is accountable) and second person (crash in which MTW rider is a victim). RESULTS: Result suggests that age, pillion passenger, type of collision, road network, and impacting vehicle increase the seriousness of a crash in both cases. However, crash day, crash time, and light condition were found to be significant in the case of second-party crashes. Similarly, crash seriousness tends to decrease in first-party crashes, whereas it increases in the context of second-party crashes. CONCLUSION: The statistical results were correlated with past studies to provide proper justification in order to provide a better understanding of small-displacement MTW fatal accidents in developing countries. Additionally, this research aids in the development of mitigation strategies and future research directions to improve the safety of MTW users.

Sen-2 LULC: Land use land cover dataset for deep learning approaches.

Land Use Land Cover (LULC) classification is pivotal to sustainable environment and natural resource management. It is critical in planning, monitoring, and management programs at various local and national levels. Monitoring changes in LULC patterns over time is crucial for understanding evolving landscapes. Traditionally, LULC classification has been achieved through satellite data by remote sensing, geographic information system (GIS) techniques, machine learning classifiers, and deep learning models. Semantic segmentation, a technique for assigning land cover classes to individual pixels in an image, is commonly employed for LULC mapping. In recent years, the deep learning revolution, particularly Convolutional Neural Networks (CNNs), has reshaped the field of computer vision and LULC classification. Deep architectures have consistently outperformed traditional methods, offering greater accuracy and efficiency. However, the availability of high-quality datasets has been a limiting factor. Bridging the gap between modern computer vision and remote sensing data analysis can revolutionize our understanding of the environment and drive breakthroughs in urban planning and ecosystem change research. The "Sen-2 LULC Dataset" has been created to facilitate this convergence. This dataset comprises of 213,761 pre-processed 10 m resolution images representing seven LULC classes. These classes encompass water bodies, dense forests, sparse forests, barren land, built-up areas, agricultural land, and fallow land. Importantly, each image may contain multiple coexisting land use and land cover classes, mirroring the real-world complexity of landscapes. The dataset is derived from Sentinel-2 satellite imagery sourced from the Copernicus Open Access Hub (https://scihub.copernicus.eu/) platform. It includes spectral bands B4, B3, and B2, corresponding to red, green, and blue (RGB) channels, and offers a spectral resolution of 10 m. The dataset also provides an equal number of mask images. Structured into six folders, the dataset offers training, testing, and validation sets for images and masks. Researchers across various domains can leverage this resource to advance LULC classification in the context of the Indian region. Additionally, it catalyzes fostering collaboration between remote sensing and computer vision communities, enabling novel insights into environmental dynamics and urban planning challenges.

A Psittacine bite and subcutaneous basidiobolomycosis: A case with a therapeutic challenge.

Basidiobolus ranarum is a saprophyte that can be found in soil, rotting vegetables, and frogs' digestive tracts. Clinically, basidiobolomycosis presents as a persistent infection of subcutaneous tissue affecting the trunk and extremities in an immunocompetent host. We describe a case of subcutaneous basidiobolomycosis in a 56-year-old immunocompetent woman farmer by occupation residing at remote part of central India. This study highlights the traumatic implantation and zoonotic potential of fungal species. Clinical suspicion of fungal etiology and timely mycology laboratory diagnostic support is key to address such cases. This case is documented to emphasize the problems of compliance to treatment specially in remote and poor patients challenging the treatment with complete cure. 2012 Elsevier Ltd. All rights reserved.

Recent progress in the stabilization of low band-gap black-phase iodide perovskite solar cells.

All-inorganic and organic-inorganic hybrid perovskite solar cells (PSCs) have taken a quantum leap owing to their high performance and low-cost solution processability. Their efficiency has been dramatically increased up to ∼26%, matching the conventional inorganic photovoltaics like monocrystalline Si (26.1%), polycrystalline Si (21.6%), CdTe (22.1%), and CIGS (22.3%). Such outstanding performance has been achieved due to their excellent optoelectronic properties, such as a direct bandgap in the visible region, a very high absorption coefficient, a long charge-carrier diffusion length, and ambipolar carrier transport characteristics. FAPbI3 (FA = formamidinium) and CsPbI3 perovskites among the pool of perovskites are recommended for solar cell applications because they meet all the requirements for photovoltaic applications. However, the fundamental problem of these perovskites is that their photoactive black phase is highly unstable under ambient conditions due to small and large sizes of Cs+ and FA+ ions, respectively. The instability of the black phase of these perovskites hinders their applications in photovoltaic devices as a high-quality light absorber layer. Several approaches have been employed to prevent the formation of the photo-inactive yellow phase or to enhance the stability of the black phase of perovskites, such as dimensional and compositional engineering, the addition of external additives, and dimensional engineering. This perspective summarizes the various methods for stabilizing the black phase of CsPbI3 and FAPbI3 perovskites at room temperature as well as their application in photovoltaic devices.

Imaging Characteristics of Tongue Hematoma and Pseudoaneurysm Following Tooth Extraction Requiring Emergency Liquid Embolization.

Our case describes the imaging characteristics of a tongue hematoma and lingual artery pseudoaneurysm following oral surgery, treated with a liquid embolic agent prior to repeat instrumentation. Identifying particular imaging cues that suggest underlying vascular pathology is essential to prevent unnecessary, potentially fatal instrumentation. A liquid embolizing agent can be used to endovascularly treat an unstable pseudoaneurysm in the oral cavity.

Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties.

Barren lands are being transformed into agricultural fields with the growing demand for agriculture-based products. Hence, monitoring these regions for better planning and management is crucial. Surveying with high-resolution RS (remote sensing) satellites like Worldview-2 provides a faster and cheaper solution than conventional surveys. In the study, the arid region comprising cropland and barrenlands are efficiently and autonomously delineated using its spectral and textural properties using state-of-the-art random forest (RF) ensemble classifiers. The textural information window size is optimized and at a GLCM (gray-level co-occurrence matrix) window size of 13, a stable trend in classification accuracy was observed. A further rise in window sizes did not improve the classification accuracy; beyond GLCM 19, a decline in accuracy was observed. Comparing GLCM-13 RF with the no-GLCM RF classifier, the GLCM-based classifiers performed better; thus, the textural information assisted in removing isolated crop-classified outputs that are falsely predicted pixel groups. Still, it also obscured information about barren lands present within croplands. Delineation accuracy was 93.8 % for the no-GLCM RF classifier, whereas, for the GLCM-13 RF classifier, an accuracy of 97.3 % was observed. Thus, overall, a 3.5 % improvement in accuracy was observed while using the GLCM RF classifier with window size 13. The textural information with proper calibration over high-spatial resolution datasets improves crop delineation in the present study. Henceforth, a more accurate cropland identification will provide a better estimate of the actual cropland area in such an arid region, which will assist in formulating a better resource management policy.

Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification:a case study for Banswara in Rajasthan, India.

Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K-means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.