One of the biggest challenges in sports medicine is the return-to-play decisions, making or breaking athletic careers. Since there are no protocols to guide team physicians and consultants for athletes with fatigue syndrome, illness, injury, or overtraining syndrome, real-time monitoring plays a crucial role in such cases. By monitoring a combination of performance (e.g., maximal lactate concentration, maximal heart rate at lactate threshold), physiological (e.g., resting heart rate and maximal heart rate), biochemical (e.g., glucose) and hormonal (e.g., cortisol) variables, there should be objective indices determining eligibility or disqualification for the ill or injured athletes, allowing rehabilitation practitioners to improve and adjust their plan accordingly on a real-time information provision basis.

Degenerative changes in the intervertebral discs can cause a significant impact on the biomechanics of the spine this can result in compression of the nerve roots or the spinal cord [1]. 

The COVID-19 pandemic appeared in late 2019 and became a major health concern with rapid transmission and very high mortality rates across the globe. Although precautionary, preventive, protective and therapeutic measures have been adopted against COVID-19, still the disease has drastically affected people. In order to overcome the challenges of the pandemic, the understanding of the route of transmission, its fusion with receptors and invasion into the human body and hacking the immune system, the viral genome was sequenced. The viral genome keeps on mutating and altering its original form into its subtypes. Moreover, age and comorbid conditions had their impact on developing the disease differing from individual to individual due to interaction varying between the host genome and virus. Considering the pathogenesis of the virus, neutralizing antibodies reduced the viral impact and severity. This review is focused on highlighting the COVID-19 genome, host genetic factors, the pathogenesis of the disease and available therapeutic measures to overcome the pandemic.

Dynamic Knee Valgus (DKV) is correlated with both, Anterior Cruciate Ligament (ACL) injury and hip and ankle disorders in female athletes and has a more significant prevalence compared with male athletes because of numerous factors. The aim of this study is to determine if the connexion between DKV, landing errors, and non-contact ACL injury and re-injury in high-performance, adult, female team sport athletes can be eliminated by changing the frontal plane movement pattern and the landing errors during the rehabilitation process (RHB), a process which was focussed on dynamic knee stability with multidimensional single-leg jump landing training, on 3D knee balance improvement and multistimulus perturbation challenges and tasks,+ eccentric & concentric exercise, strength & conditioning, aerobic training that lasted from 26 - 44 weeks.Assessing and eliminating-reducing DKV during the RHB is mandatory in lowering the re-rupture rates in female professional athletes after ACL surgery and in preventing opposite knee trauma.

Climate change has left its traces on the planet in the last 3 decades. Most of the influenced areas were occupied in the Middle East which was degraded to desertification phenomena today. The per capita share of renewable water has been reduced to less than the poverty line of 1,000 m3/(capita) and in some Arab countries, to less than the extremepoverty line of 500 m3/(capita).

Indonesia is the fourth largest country in the world by population and has made considerable progress since the 1960s in terms of economic growth and structural transformation. But in recent years it has become apparent that the health of the nation is far from satisfactory. The purpose of this article is to review recent literature on health problems in Indonesia and to outline the policy challenges which face the government. Since 2001, the provision of primary health care has been decentralized to sub-provincial levels of government but they lack often lack the resources, and trained staff to provide adequate services to rural populations in particular. The literature suggests that while most health indicators have shown some improvement in recent years, there are still large variations by region and social class. The country is still far from achieving the goal of universal health coverage.

Parkinson’s disease is a progressive and debilitating neurodegenerative disorder affecting millions of people worldwide. The disease is characterized by motor symptoms such as tremors, rigidity and postural instability, as well as non-motor symptoms such as depression and cognitive impairment. While there is no cure for Parkinson’s disease, there are various treatments available to manage symptoms and improve quality of life for patients.This case study examines a 65-year-old retired accountant, Mr. John Smith, who was diagnosed with Parkinson’s disease five years ago. Mr. Smith has been treated with a combination of medications, including levodopa and carbidopa and physical therapy to manage his symptoms. However, his symptoms have not significantly improved.This literature review explores the current research on Parkinson’s disease, including its pathophysiology, diagnosis and treatment. Parkinson’s disease is caused by the degeneration of dopamine-producing neurons in the brain, leading to a depletion of dopamine and the accumulation of alpha-synuclein protein, oxidative stress and inflammation. Diagnosis is based on clinical symptoms, neurological examination and response to dopaminergic therapy. Treatment focuses on managing symptoms, with medications and non-pharmacological interventions such as exercise and physical therapy. Deep brain stimulation is a surgical treatment option that has been shown to be effective in managing motor symptoms.While there is currently no cure for Parkinson’s disease, ongoing research into its pathophysiology and treatment holds promise for improving outcomes for patients. This case study highlights the importance of early diagnosis and personalized treatment plans for patients with Parkinson’s disease.

The COVID-19 Pandemic, which began in March 2020, and its associated surges, had an immense impact on our medical staff and their ability to perform their daily duties.The COVID-19 Pandemic necessitated hospital modifications, including the expansion of the Emergency Department, ICU, and Isolation units. The overwhelmed staff and overburdened ER and ICU required adjustments to deal with the Inpatient impasse. For example, temporary patient care rooms needed to be set up for the overflow of patients. The tsunami of the Pandemic almost overwhelmed our hospital.With the challenges presented to our hospital during the Pandemic, we needed a fresh perspective to our multi-disciplinary approach. Thus, we fathomed that the use and cost-effectiveness of both Dalbavancin, a long-acting lipoglycopeptide antibiotic combined with the diagnostic Moleculight Imaging Device would be a sound decision.We hypothesized that with the use of this cost-effective antibiotic in conjunction with the use of the Moleculight Imaging Device, with its fluorescent imaging, we could detect the presence of bacteria and assist our wound treatment and decision-making.A shift in patients from the Emergency Room/Inpatient Department to the Outpatient Department/Wound Care Center was made possible with the use of these two novel products.

Cancer prognosis reflects a complex biological process measured by multiple types of omics data. Deep learning frameworks have been proposed to integrate multi-omics data and predict patient outcomes in different cancer types, potentially revolutionizing cancer prognosis with superior performance. This minireview summarizes the advances in the strategies for multi-omics data integration and the performance of different deep learning models in prognosis prediction of diverse cancer types using multi-omics data published in the past 18 months. The challenges and limitations of deep learning models for predicting cancer outcomes based on multi-omics data are discussed.

The nanometer-scale spatial organization of immune receptors plays a role in cell activation and suppression. While the connection between this spatial organization and cell signaling events is emerging from cell culture experiments, how these results translate to more physiologically relevant settings like the tumor microenvironment remains poorly understood due to the challenges of high-resolution imaging in vivo. Here we perform super-resolution immunofluorescence microscopy of human melanoma tissue sections to examine the spatial organization of the immune checkpoint inhibitor programmed cell death 1 (PD-1). We show that PD-1 exhibits a variety of organizations ranging from nanometer-scale clusters to more uniform membrane labeling. Our results demonstrate the capability of super-resolution imaging to examine the spatial organization of immune checkpoint markers in the tumor microenvironment, suggesting a future direction for both clinical and immunology research.

Coronavirus disease (COVID-19) is a viral pneumonia that is found in China and has spread globally. Early diagnosis is important for effective and timely treatment. Thus, many ongoing studies attempt to solve key COVID-19 problems such as workload classification, detection, and differentiation from other pneumonia and healthy lungs using different imaging modalities. Researchers have identified some limitations in the deployment of deep learning methods to detect COVID-19, but there are still unmet challenges to be addressed. The use of binary classifiers or building classifiers based on only a few classes is some of the limitations that most of the existing research on the COVID-19 classification problem suffers from. Additionally, most prior studies have focused on model or ensemble models that depend on a flat single-feature imaging modality without using any clinical information or benefiting from the hierarchical structure of pneumonia, which leads to clinical challenges, and evaluated their systems using a small public dataset. Additionally, reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays. Radiologists, computer scientists, and physicians all need to come to an understanding of these interdisciplinary issues. This article first highlights the challenges of deep learning deployment for COVID-19 detection using a literature review and document analysis. Second, it provides six key recommendations that could assist future researchers in this field in improving the diagnostic process for COVID-19. However, there is a need for a collective effort from all of them to consider the provided recommendations to effectively solve these issues.

There are few reported cases of meningitis in pregnancy and presentation can be variable in each patient which may cause diagnostic challenges and potential delays in treatment. Here, we discuss a case of aseptic meningitis - a 36 years old Gravida 2 Para 1 pediatric physician with “the worst headache of her life” at 31+5 weeks of gestation. At presentation, she did not have any of the classic triads of meningitis (fever, neck stiffness, and/or a change in mental status) and did not report any neurological symptoms. In view of persistent headache, development of pyrexia, and evolving symptoms of photophobia, she was referred to a neurology specialist, and a lumbar puncture was performed. Findings were in line with meningitis and the polymerase chain reaction confirmed enterovirus meningitis. Although aseptic meningitis is rare, it should be considered as a differential diagnosis in patients with persistent complaints, development of associated symptoms of pyrexia, photophobia, and neck pain, as well as in patients with increased risk of exposure to viral illnesses.

The 2019 COVID-19 pandemic caused by SARS-CoV-2 has resulted in many fatalities worldwide. Despite various types of supportive care, mortality rates for patients with comorbidities remain high. To explore alternative treatment options, interferons (IFNs) have emerged as promising therapeutic drugs for SARS-CoV-2. This review aims to investigate the potential of IFNs as a drug with details on their mechanisms of action, and available data on their use with ongoing clinical trials, results, potential limitations, and challenges.  Recently published research articles, which are systematically searched through online databases, have been selected and found that IFNs have colossal potential in treating SARS-CoV-2 infection by modulating the host’s immune response and inhibiting viral replication and decreasing the severity of disease and hospitalization (p = 0.03, ± 0.05) and (p = 0.04, ± 0.05) respectively. However, due to less available data, more controlled and randomized trials are needed to confirm the efficacy and safety of IFN therapy.  The optimal dosing and duration of IFN therapy also remain to be determined. Although further research is needed the wait for ongoing clinical trial results under investigation is also important for a better understanding of IFN therapy.

Continuous noninvasive cuffless blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuffless BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ different measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the effective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuffless BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.

Down syndrome is a common chromosomal abnormality that affects individuals in their physical, cognitive, and social development. The diagnosis of this syndrome in a newborn can be challenging for parents as it may disrupt family dynamics. In this study, we describe the different aspects of the impact of Down syndrome on parents, including psychological, social, and familial impacts. 55 cases participated in this survey; the average age of parents was 34 years old, and mothers represented 96% of the participants. 70% of cases were from urban areas. The average time between diagnosis and parental evaluation was 32.7 months. In 57% of families, the diagnosis of Down syndrome was established only after the third month of pregnancy, and only 5% obtained it during the prenatal period. Depression was reported in 32% of mothers at the time of diagnosis, while one mother exhibited persistent denial of her child’s disability. 30% of families indicated that their child with Down syndrome was the source of tension within the couple and that learning difficulties were the main factor of tension, while 1% eventually divorced. 5% of parents regretted having a child with Down syndrome. All mothers expressed concerns about their child’s future and are in favor of the establishment of a national screening program for trisomy 21 and dedicated learning centers to ensure social and professional integration.

Introduction: Congenital heart disease is a leading cause of neonatal mortality linked to birth defects. Despite the widespread availability of prenatal screenings, detection rates remain low. Accurate early detection of these lesions is pivotal to reducing neonatal morbidity and mortality.Methods: In this case, we present a neonate who experienced sudden cardiac death due to a rare, undiagnosed congenital cardiac anomaly - the bilateral absence of coronary artery ostium. Discussion: This case highlights the importance of prenatal detection of congenital cardiac anomalies. While fetal echocardiography is frequently utilized, it only identifies CHD in 36-50% of cases. This is attributed to inadequate imaging procedures, varied operator skills, and regional discrepancies. Early detection of severe CHD is essential for specialized treatment, thereby mitigating neonatal health risks and improving survival rates.Conclusion: Prenatal detection of CHD, especially coronary anomalies, continues to pose significant challenges. There is a pressing need to establish and enforce standardized protocols for fetal echocardiography aimed at these anomalies. To enhance care and improve outcomes, a joint effort between academic institutions and community centers is encouraged.Learning Objectives: •    Congenital coronary artery anomalies are a significant cause of sudden cardiac death in children.•    The absence of a coronary artery ostium is known to be associated with other congenital heart diseases, particularly pulmonary atresia with an intact ventricular septum. However, isolated coronary disease has also been reported in this case.•    Prenatal echocardiography is a valuable tool for diagnosing congenital heart disease. However, certain limitations may be encountered when diagnosing coronary artery anomalies.

Forensic handwriting analysis is a specialized field within forensic science that aims to identify and compare handwriting samples for investigative purposes. This scientific note provides an overview of the fundamental principles, methodologies, and techniques involved in the examination of handwriting evidence. The note explores the importance of handwriting analysis in criminal investigations, discusses the underlying scientific principles, and highlights the challenges faced by forensic experts in this field. Additionally, it explores the advancements in technology that have aided the analysis of handwriting and conclude with the potential future directions of research in forensic handwriting analysis. This study focuses on the natural variation that occurs due to the unusual surfaces of tables and bricks, the variation occurs mostly in the class characteristics of the person like slant, speed, line quality, alignment, etc. Samples are collected on two different surfaces; a table and a wall (smooth and rough surfaces). And on the basis of these two surfaces, the variations in the characteristics of the individual are observed. The significance of the research is to find out the range of natural variation that occurs in the individual handwriting whenever there is a change in the writing surface.

Background: The COVID-19 pandemic has resulted in significant burdens globally. Healthcare workers (HCWs), at the heart of the unparalleled crisis of COVID-19, face challenges treating patients and doing testing for COVID-19: reducing the spread of infection; developing suitable short-term strategies; and formulating long-term plans. We aimed to assess the psychological impact of COVID-19 on Laboratory health staff. Material and methods: Between February - March 2021, 72 laboratory staff workers of a tertiary care teaching hospital were invited to fill out a questionnaire regarding concerns and worries about the novel coronavirus pandemic, along with a coping scoring system and General health questionnaire level (GHQ-12) survey. Results: Out of 72 laboratory health staff who completed the survey questionnaire, 10 were faculty members, 17 were residents (including senior residents, junior residents, and demonstrators), 39 were lab technicians, followed by 4 were attendants and 2 were data operators. Laboratory staff workers with an age group range from 30 years - 60 years had a higher level of depression symptoms than respondents with 20 years - 29 years of age. Similarly, the symptoms of anxiety were noted to be significantly higher among female respondents and respondents with age >30yrs. The most frequent concern was transmitting the infection to family than to themselves only. A considerable number of laboratory staff workers utilized online psychological resources to deal with their psychological distress.Conclusion: The findings of this survey recognize the various problems faced by laboratory health workers during the period of COVID-19 which affect their working ability. Therefore, in the future, we have to implement such strategies that enhance the performance of laboratory workers, boost their energy level, and encourage them to take care of themselves, in times of such crisis.

Ethnomedicine, synonymous with traditional medicine, is a crucial healthcare system practiced by various ethnic groups worldwide, especially among those with limited access to modern Western medicine. This study explores the rich bio-cultural diversity of the North Central Western Ghats in Karnataka, India, which harbors diverse ethnomedicinal practices. The region's tropical forests are home to an extensive array of plant species, with over 600 endemic to southern India and 95 exclusively endemic to Karnataka. The research focuses on documenting and analyzing the traditional knowledge of local communities regarding the use of plants for treating various human diseases. However, this task presents significant challenges and requires collaborative efforts from the government, NGOs, and Herbal Drug Companies. Over the last decade, ethnomedicinal studies have seen a rise, but there is still limited understanding of ethnomedicine's role in the traditional healthcare system in India. The forests of North Central Western Ghats, including Agumbe, Arbail Ghat, Chorla Betta, and others, exhibit a combination of deciduous and evergreen vegetation. These forests hold a variety of medicinal plants, adding to the region's bio-cultural richness. Scientific validation of the locally used ethnomedicinal plants further supports the development of herbal drug formulations with the support of the Ministry of Ayush, enabling the conservation and sustainable utilization of threatened and endangered species. The study emphasizes the importance of preserving traditional healers' knowledge and promoting collaboration for the preservation of ethnomedicinal practices in the region.

Photobiomodulation (PBM) is an effective method for treating neuropsychiatric diseases, yet its universal acceptance and utilization remain constrained. In this Letter to the Editor/Expert Opinion, we address the challenges confronting PBM researchers and pioneers, striving to establish universal confidence in its exclusive application for neuropsychiatric patient care. Despite encouraging animal experiments and selecting positive human clinical trial outcomes, PBM’s widespread acceptance of PBM is hindered by factors including limited clinical studies and clashes with established therapies, such as drug therapy and psychotherapy.To overcome these obstacles and broaden PBM’s adoption and application of PBM in neuropsychiatry, we propose a combinatorial therapy approach. By integrating PBM with interventions such as Cognitive Behavioral Therapy (CBT) or drug therapy, a cumulative effect can be attained, benefiting both patients and therapists. Patients gain access to diverse treatment options and experience synergistic effects of combined therapies, thereby enhancing outcomes. Therapists benefit from expanded intervention choices and improved quality of patient care.This study introduces a novel strategy of amalgamating PBM with recognized interventions, such as CBT and drug therapy, to address existing challenges. This combined approach offers a practical solution to augment PBM acceptance and usage in the realm of neuropsychiatry.To endorse this strategy, a shift in the research direction is imperative. Future studies should investigate the amalgamated use of PBM with other neurointerventions, such as CBT or drug therapy. Robust clinical trials contrasting groups like “PBM + CBT” and “PBM (sham) + rTMS” are vital to persuade clinicians and patients toward embracing combined PBM therapies.Although PBM’s eventual autonomy as a neuropsychiatric treatment is an overarching goal, the present combination therapy approach proves practical, inevitable, and mutually beneficial. By bridging conventional therapies with PBM, this strategy may facilitate wider acceptance and utilization in neuropsychiatry.