RV in COPD - The complicated matters of the heart - Correlation of ECHO and biomarker with COPD severity and outcome.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of cardiovascular involvement, which is among the leading causes of morbidity and mortality worldwide. Echocardiography (ECHO) could be a reliable, non-invasive tool for predicting the risk of cardiovascular modalities in patients with COPD. Combining the ECHO parameters with highly selective cardiac troponin could predict the severity and outcome of patients with COPD. METHODS: This prospective observational study was conducted at a tertiary care hospital in South India. All patients who met the criteria were included. Patients with other concomitant chronic lung diseases were excluded. An echocardiographic examination was performed, and blood samples for hs-Tnt were taken on admission for patients admitted with COPD. Categorical variables were analyzed using Pearson's Chi-square test, and the T-test was used to compare the means. One-way analysis of variance (ANOVA) followed by the Bonferroni multiple comparison tests was done to compare different echo parameters concerning COPD severity. RESULTS: The mean tricuspid annulus plane systolic excursion (TAPSE) and right ventricle (RV) fraction area change (FAC) values were lower with the increase in the disease severity (P < 0.001). There was a significant increase in the mean systolic pressures in the right atrium and ventricle in patients with severe COPD (P < 0.001). The mean hs-TnT values were significantly higher in patients with severe COPD (18.86 ± 18.12) and correlated well with the increase in the severity of the disease (P < 0.001). Changes in the echo parameters, such as mean TAPSE and RV FAC values, negatively correlated with COPD severity. There was an increase in systolic pressure in both atria and ventricles with the progression of COPD. Troponin helped predict mortality during hospitalization. CONCLUSION: Comprehensive echocardiographic parameters, such as TAPSE and RV FAC, help assess the disease's severity, predict mortality, and evaluate whether the proper ventricular function is reliable. Troponin is a valuable adjunct that is an independent and strong predictor of overall mortality in patients with COPD.

Tuning the Fermi Level of Graphene by Two-Dimensional Metals for Raman Detection of Molecules.

Graphene-enhanced Raman scattering (GERS) offers great opportunities to achieve optical sensing with a high uniformity and superior molecular selectivity. The GERS mechanism relies on charge transfer between molecules and graphene, which is difficult to manipulate by varying the band alignment between graphene and the molecules. In this work, we synthesized a few atomic layers of metal termed two-dimensional (2D) metal to precisely and deterministically modify the graphene Fermi level. Using copper phthalocyanine (CuPc) as a representative molecule, we demonstrated that tuning the Fermi level can significantly improve the signal enhancement and molecular selectivity of GERS. Specifically, aligning the Fermi level of graphene closer to the highest occupied molecular orbital (HOMO) of CuPc results in a more pronounced Raman enhancement. Density functional theory (DFT) calculations of the charge density distribution reproduce the enhanced charge transfer between CuPc molecules and graphene with a modulated Fermi level. Extending our investigation to other molecules such as rhodamine 6G, rhodamine B, crystal violet, and F16CuPc, we showed that 2D metals enabled Fermi level tuning, thus improving GERS detection for molecules and contributing to an enhanced molecular selectivity. This underscores the potential of utilizing 2D metals for the precise control and optimization of GERS applications, which will benefit the development of highly sensitive, specific, and reliable sensors.

Efficacy and adverse effects of insulin versus plasmapheresis in patients with hypertriglyceridemia-3-induced acute pancreatitis: a systematic review and meta-analysis.

Background: Hypertriglyceridemia is a common cause of acute pancreatitis (AP). This literature review compared the effectiveness and adverse events of insulin therapy, with or without heparin, and plasmapheresis, in reducing triglyceride levels in patients with hypertriglyceridemia-induced AP. Methods: Systematic reviews, meta-analyses, evidence syntheses, editorials, commentaries, protocols, abstracts, theses and preprints were excluded. Review Manager was used to conduct the meta-analysis. The literature search yielded 2765 articles, but only 5 were included in the systematic review and meta-analysis and the total number of participants in the review was 269. Results: From this study's analysis, insulin ± heparin was more successful in reducing triglyceride levels than plasmapheresis (standardized mean difference -0.37, 95% confidence interval [CI] 0.99 to 0.25; P=0.25). Insulin ± heparin therapy had a lower mortality rate than plasmapheresis (risk ratio [RR] 0.70, 95%CI 0.25-1.95). Hypotension, hypoglycemia, and acute renal failure were less common in the plasmapheresis therapy group than in insulin ± heparin therapy (RR 1.13, 95%CI 0.46-2.81, RR 3.90, 95%CI 0.45-33.78, and RR 0.48, 95%CI 0.02-13.98 for hypotension, hypoglycemia, and acute renal failure, respectively). Conclusions: This study found no significant difference in mortality between insulin ± heparin therapy and plasmapheresis used for the reduction in triglyceride levels. It is notable that no substantial differences were observed in the most common side-effects encountered during these therapies, thus indicating non-inferiority.

The Role of Gut Microbiota in Neurodegenerative Diseases: Current Insights and Therapeutic Implications.

Small microscopic entities known as microbes, having a population of hundreds of billions or perhaps even in trillions, reside in our gastrointestinal tract. A healthy immune system, digestion, and creation of vitamins and enzymes are all thanks to these microbes. However, new research has shown a hitherto unrecognized connection between the microbiota of the intestines and the genesis of neurodegenerative diseases. Neurons in the CNS gradually deteriorate in neurodegenerative illnesses like multiple sclerosis and Parkinson's disease (PD). This deterioration impairs cognitive and physical function. Amyotrophic lateral sclerosis (ALS), PD, and Alzheimer's disease (AD) are just a few examples of neurodegenerative illnesses that pose a serious threat to world health and have few effective treatments. Recent research suggests that the gut microbiota, a diverse microbial population found in the gastrointestinal system, may substantially impact the cause and development of various diseases. The discovery of altered gut microbiota composition in people with these illnesses is one of the most critical lines of evidence connecting gut microbiota dysbiosis to neurodegenerative diseases. AD patients have a distinct characteristic of having a particular microbiota profile. In addition, an excess population of a specific microbe data profile is seen as compared to a healthy individual. Similar changes in the gut microbiota composition have been noted in people with multiple sclerosis and PD. The latest study indicates the potential that dysbiosis, a condition characterized by alteration in the intestinal microbiota's makeup and functioning, may have an effect on the onset and progression of neurodegenerative diseases, including PD and multiple sclerosis. In order to emphasize any potential underlying mechanisms and examine potential treatment repercussions, the review article's goal is to summarize current knowledge about the connection between gut microbiota and neurodegenerative disorders. The review article aims to summarize current knowledge about the connection between gut microbiota and neurodegenerative disorders, highlighting potential underlying mechanisms and examining potential treatment repercussions.

Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors 2022, 22, 5160.

In the original publication [...].

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.

Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models.

This study proposes an intelligent health prediction and fault prognosis of the endodontic file during the root canal treatment. Root canal treatment is the procedure of disinfecting the infected pulp through the canal with the help of an endodontic instrument. Force signals are acquired with the help of a dynamometer during the canal preparation, and statistical features are extracted. The extracted features are selected through the window-wise feature extraction process. Characteristic features for endodontic file prognostics include time-domain features of the signals are evaluated. The extracted feature has inappropriate information, that is, noise between the signals; hence the smoothing of the feature is required at this stage to observe a trend in the signals. Based on the smoothing feature and post-processing of the feature, defined the health index to calculate the health condition of the endodontic instruments. A machine learning algorithm and exponential degradation model are used to predict the health of the endodontic instrument during the root canal treatment. This model is used to forecast the degradation of the endodontic file so that actions can be taken before actual failures happen. The proposed methodology can analyze the failures and micro-crack initiation of the endodontic instruments. Endodontics practitioners can use the machine learning models as well as an exponential model for estimating the health condition of the endodontic instrument. This study may help the clinician to progress the efficiency of the root canal treatment and the competence of the endodontic instruments.

Effects of Angiotensin Receptor-Neprilysin Inhibitors Versus Enalapril or Valsartan on Patients With Heart Failure: A Systematic Review and Meta-Analysis.

Recent studies have focused on treating heart failure, primarily mitigating symptoms and reducing the risk of mortality and other cardiovascular complications. A promising new treatment approach involves using LCZ696, an angiotensin receptor-neprilysin inhibitor (ARNI) comprising sacubitril and valsartan. This treatment is superior to the conventional drugs enalapril or valsartan in patients diagnosed with heart failure. A systematic search was conducted on PubMed, the Cochrane Library, and Elsevier's ScienceDirect databases to identify studies comparing sacubitril/valsartan with other drugs in heart failure patients with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). The analyses were conducted using the random-effects model. The study's primary outcomes included all-cause mortality, death from cardiovascular causes, first hospitalization for heart failure, congestive heart failure, and changes in the Kansas City Cardiomyopathy Questionnaire (KCCQ) clinical score. The pooled analysis showed that treatment with the sacubitril/valsartan combination was associated with a significantly decreased rate of first hospitalization for heart failure (RR: 0.86; 95% CI: 0.79, 0.98, p: 0.03; I2: 57%) and significantly increased KCCQ clinical score (WMD: 2.20; 95% CI: 0.33, 4.06, p: 0.02; I2: 100%). However, the two groups had no significant difference in all-cause mortality (RR: 0.90; 95% CI: 0.80, 1.01, p: 0.08; I2: 20%), death from cardiovascular causes (RR: 0.96; 95% CI: 0.87, 1.05, p: 0.34; I2: 0%), or congestive heart failure (RR: 0.97; 95% CI: 0.75, 1.25, p: 0.19; I2: 38%). The research findings suggest that sacubitril/valsartan (LCZ696) reduces hospitalizations due to heart failure and improves KCCQ clinical scores. This treatment also reduces the decline in renal function and side effects associated with enalapril or valsartan. Nonetheless, further high-quality randomized controlled trials with large sample sizes are needed to assess other impacts of this therapy on heart failure patients. Overall, the use of LCZ696 represents a promising new approach to the treatment of heart failure.

Impact of improving infection control and antibiotic stewardship practices on nosocomial infections and antimicrobial resistance in an oncology centre from India.

BACKGROUND: Improving basic infection control (IC) practices, diagnostics and anti-microbial stewardship (AMS) are key tools to handle antimicrobial resistance (AMR). MATERIALS AND METHODS: This is a retrospective study done over 6 years (2016-2021) in an oncology centre in North India with many on-going interventions to improve IC practices, diagnostics and AMS. This study looked into AMR patterns from clinical isolates, rates of hospital acquired infections (HAI) and clinical outcomes. RESULTS: Over all, 98,915 samples were sent for culture from 158,191 admitted patients. Most commonly isolated organism was E. coli (n â€‹= â€‹6951; 30.1%) followed by Klebsiella pneumoniae (n â€‹= â€‹5801; 25.1%) and Pseudomonas aeroginosa (n â€‹= â€‹3041; 13.1%). VRE (Vancomycin resistant Enterococcus) rates fell down from 43.5% in Jan-June 2016 to 12.2% in July-Dec 2021, same was seen in CR (carbapenem resistant) Pseudomonas (23.0%-20.6%, CR Acinetobacter (66.6%-17.02%) and CR E. coli (21.6%-19.4%) over the same study period. Rate of isolation of Candida spp. from non-sterile sites also showed reduction (1.68 per 100 patients to 0.65 per 100 patients). Incidence of health care associated infections also fell from 2.3 to 1.19 per 1000 line days for CLABSI, 2.28 to 1.88 per 1000 catheter days for CAUTI. There was no change in overall mortality rates across the study period. CONCLUSION: This study emphasizes the point that improving compliance to standard IC recommendations and improving diagnostics can help in reducing the burden of antimicrobial resistance.

Ondansetron-induced QT prolongation among various age groups: a systematic review and meta-analysis.

BACKGROUND: Ondansetron is a selective 5-hydroxytryptamine type 3 serotonin-receptor antagonist with antiemetic properties used inadvertently in the emergency department for controlling nausea. However, ondansetron is linked with a number of adverse effects, including prolongation of the QT interval. Therefore, the purpose of this meta-analysis was to assess the occurrence of QT prolongation in pediatric, adult, and elderly patients receiving oral or intravenously administered ondansetron. METHODS: A thorough electronic search was conducted on PubMed (Medline) and Cochrane Library from the databases' inception to August 10, 2022. Only those studies were considered in which ondansetron was administered orally or intravenously to participants for the treatment of nausea and vomiting. The prevalence of QT prolongation in multiple predefined age groups was the outcome variable. Analyses were conducted using Review manager 5.4 (Cochrane collaboration, 2020). RESULTS: A total of 10 studies involving 687 ondansetron group participants were statistically analyzed. The administration of ondansetron was associated with a statistically significant prevalence of QT prolongation in all age groups. An age-wise subgroup analysis was conducted which revealed that the prevalence of QT prolongation among participants younger than 18 years was not statistically significant, whereas it was statistically significant among participants aged 18-50 years and among patients older than 50 years. CONCLUSIONS: The present meta-analysis provides further evidence that oral or intravenous administration of Ondansetron may lead to QT prolongation, particularly among patients older than 18 years of age.

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks.

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

Knowledge and Awareness About Diabetes Mellitus Among Urban and Rural Population Attending a Tertiary Care Hospital in Haryana.

BACKGROUND: Diabetes mellitus (DM) is one of the fastest-growing public health problems in the twenty-first century. The ignorance among people about their disease may be related to their low socioeconomic status and lack of quality education available to them about the disease. It is a serious condition leading to several complications if the individual does not follow up regularly for check-ups and blood sugar monitoring. Lifestyle modifications such as a healthy diet, regular exercise, reducing weight, stress management, and smoking cessation can play a critical role in managing diabetes and improving the health and well-being of diabetic patients. Thus, through this study, we want to assess and create awareness among diabetic patients. METHODOLOGY: It is a hospital-based cross-sectional study conducted at a tertiary care hospital on diagnosed cases of DM. The patients aged 18 years or above of either gender who had already been diagnosed with DM type 1 and type 2 were included, and patients with gestational DM were excluded from the study. Informed consent was taken from the patients, and all the required details were obtained using a well-structured questionnaire. After obtaining all the answers, the level of knowledge and awareness was analyzed, and the data was entered into an MS Excel sheet (Microsoft, Redmond, Washington) and analyzed by Statistical Package for the Social Sciences (SPSS) version 22.0 (IBM Corp., Armonk, NY). RESULTS: In our study, the maximum prevalence of diabetes was seen in males (55.5%) than females (44.5%), and the mean age of our study population was 53.3 ± 16.4 years. In our study, participants from rural areas made up the majority (59%) compared to those from urban areas (41%), and the majority of participants had a high school education. Among 211 diabetics, about 84%, 79%, and 41% of the patients knew about diabetes, symptoms of diabetes, and complication of diabetes. Only 18% of the patients were aware of the symptoms of hypoglycemia, and 38% of the patients possess their own glucometers and monitor their blood sugar levels on a regular basis. Merely 38% of the diabetics were aware of the various DM treatment choices. About 52% of patients had some awareness of insulin therapy. Out of 211 patients, about half skipped their antidiabetic prescriptions, and of those, 22% took a double dose the next day. A total of 121 patients (57%) combined the use of alternative and allopathic medications, and among these, 22% of patients had replaced the allopathic with alternative medicines. Almost 53% of patients had a positive family history of diabetes; 54% of patients believe that obesity is unrelated to diabetes, and 79% of diabetics are aware of the lifestyle changes that must be done for diabetes. Almost 67% of the patients believed that diabetes could be permanently treated, and 84% of patients believed that eating too much sugar caused their diabetes. CONCLUSION: In our study, a significant number of patients suffering from diabetes had less knowledge and awareness about it. The prevalence of myths about the onset of diabetes was noticeably higher among diabetic patients. It was observed that a greater number of patients were shifting to alternative medications instead of allopathic ones, and in the long run, it can lead to various complications. Therefore, there is an immediate need to promote awareness about diabetes among the general population.

Brain Tumor Classification Using Deep Neural Network and Transfer Learning.

In the field of medical imaging, the classification of brain tumors based on histopathological analysis is a laborious and traditional approach. To address this issue, the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs), has become a popular trend in research and development. Our proposed solution is a novel Convolutional Neural Network that leverages transfer learning to classify brain tumors in MRI images as benign or malignant with high accuracy. We evaluated the performance of our proposed model against several existing pre-trained networks, including Res-Net, Alex-Net, U-Net, and VGG-16. Our results showed a significant improvement in prediction accuracy, precision, recall, and F1-score, respectively, compared to the existing methods. Our proposed method achieved a benign and malignant classification accuracy of 99.30 and 98.40% using improved Res-Net 50. Our proposed system enhances image fusion quality and has the potential to aid in more accurate diagnoses.

Ocular Manifestations in Pregnancy-Induced Hypertension at a Tertiary Level Hospital in Karnataka, India.

Introduction Pregnancy-induced hypertension (PIH) is a hypertensive disorder in pregnancy that occurs after 20 weeks of pregnancy in the absence of previously known hypertension. PIH is a common and serious complication accompanying pregnancy. Pre-eclampsia and eclampsia are multisystem disorders that can involve end organs like kidneys, liver, eyes, haematopoietic system and placenta. Though ocular involvement is not uncommon in PIH, ocular examination is not always done in all cases of PIH. Timely detection of changes in retinal vasculature can be a hint to the underlying changes in the vascular system of the various end organs of the human body including placental circulation. Adequate management of PIH is very important for both fetal and maternal well-being. Aim To evaluate the ocular manifestations in women affected by PIH (mild pre-eclampsia, severe pre-eclampsia and eclampsia) presenting to a tertiary-level hospital. Methodology This was a hospital-based cross-sectional study carried out for a period of one year at a tertiary-level hospital. A total of 120 subjects diagnosed as cases of pre-eclampsia/eclampsia admitted to the eclampsia ward of the obstetric unit formed the study population. After taking history, a detailed ocular examination was done for all patients and the findings were noted. Results The mean age of the study population was 31.91 ± 4.38 years (range 21 to 39 years). The mean gestational age was 30.89 ± 3.98 weeks. Fifty-three (44.17%) were primigravida, 64 (53.33%) were multiparous, and three (2.5%) were grand multiparous. Sixty-two (51.67%) had mild pre-eclampsia, 50 (41.67%) had severe pre-eclampsia and eight (6.67%) had eclampsia. The mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) recorded in the study were 155.32 ± 11.89 mmHg and 104.3 ± 11.41 mmHg respectively. Ocular symptoms were present in 43 (35.83%) participants. Blurring of vision (19.17%) was the commonest ocular symptom observed in the study population followed by photopsia (13.33%), diplopia (9.17%), intermittent loss of vision (5.83%), ocular pain (6.67%), and scotoma (1.67%). Systemic symptoms included headache (11.67%), epigastric pain (3.33%), and nausea (5%). Anterior segment findings like conjunctival congestion, lid edema, and subconjunctival hemorrhage each accounted for 1.67% of the study population. Fundal changes were present in 33.33% of cases. Arteriolar narrowing was the commonest fundal finding amounting to 15.83%, followed by arteriovenous (AV) crossing changes also in 15.83%, cotton wool spots in 5.83%, retinal haemorrhages in 8.33%, papilledema in 2.5%, and choroidal infarcts in 1.67% participants. Grade 1 hypertensive retinopathy was observed in 15.83% of participants, grade 2 in 8.33% of participants, grade 3 in 6.67% of participants and grade 4 in 2.5% of participants. The mean SBP and mean DBP were high among those with fundal changes (163.35 ± 10.25 mmHg and 111.15 ± 10.29 mmHg) compared to those without fundal changes (151.3 ± 10.58 mmHg and 100.88 ± 10.41 mmHg). This was statistically significant. Proteinuria showed significant correlation with retinal changes. Conclusion The retinal vasculature changes correlate with the severity of hypertension, hence, it is very important to seek ophthalmologic opinion for evaluation, diagnosis and prompt management of PIH.

Scheuermann's Disease in Young Adults: A Case Report.

Scheuermann kyphosis, also known as Scheuermann disease, juvenile kyphosis, or juvenile discogenic disease, is a condition involving an abnormal, excessive curvature of the spine. It involves both the vertebral bodies and discs of the spine and is characterized by anterior wedging of greater than or equal to 5 degrees in three or more adjacent vertebral bodies. Type 1 Scheuermann's disease involves the thoracic spine, whereas type 2 involves both the thoracic and lumbar spine. Although no definitive cause for Scheuermann's disease has been found, we have reported a case that may explain further about this disease. This article elucidates a case of a 19-year-old boy experiencing pain in the lower back and showing various signs and symptoms of Scheuermann's disease and the diagnosis and steps taken by doctors toward its treatment.

Networked control system with MANET communication and AODV routing.

The industries are presently exploring the use of wired and wireless systems for control, automation, and monitoring. The primary benefit of wireless technology is that it reduces the installation cost, in both money and labor terms, as companies already have a significant investment in wiring. The research article presents the work on the analysis of Mobile Ad Hoc Network (MANET) in a wireless real-time communication medium for a Networked Control System (NCS), and determining whether the simulated behavior is significant for a plant or not. The behavior of the MANET is analyzed for Ad-hoc on-demand distance vector routing (AODV) that maintenances communication among 150 nodes for NCS. The simulation is carried out in Network Simulator (NS2) software with different nodes cluster to estimate the network throughput, end-to-end delay, packet delivery ratio (PDR), and control overhead. The benefit of MANET is that it has a fixed topology, which permits flexibility since mobile devices may be used to construct ad-hoc networks anywhere, scalability because more nodes can be added to the network, and minimal operating expenses in that no original infrastructure needs to be developed. AODV routing is a flat routing system that does not require central routing nodes. As the network grows in size, the network can be scaled to meet the network design and configuration requirements. AODV is flexible to support different configurations and topological nodes in dynamic networks because of its versatility. The advantage of such network simulation and routing behavior provides the future direction for the researchers who are working towards the embedded hardware solutions for NCS, as the hardware complexity depends on the delay, throughput, and PDR.

Serpentine Supra-venous Hyperpigmentation "Badge of Courage" in Fight Against Cancer: An Brief Review.

Persistent serpentine supra-venous hyperpigmentation (PSSH) describes a hyperpigmentation of the skin overlying peripheral veins with characteristic of underlying vessels that are patent. It has been described most commonly after injection of chemotherapeutic drugs. We describe a 44 year old man with diagnosed case of Ca stomach on FOLFOX based chemotherapy. After the 1st cycle of Chemotherapy he developed serpentine supra-venous hyperpigmentation. Introduction: Conventional chemotherapy agents commonly cause infusion-site lesions, such as chemical cellulitis due to drug extravasation and evanescent eruptions.(1) 5-Fluorouracil (5-FU) is a cytotoxic agent used mostly in combination to treat a variety of malignant disorders. Hyperpigmentation is a rare side effect occurring with 5-FU infusions; it has been reported in 2-5% of patients. Various types of pigmentary abnormalities have been reported with 5-FU use such as diffuse hyperpigmentation of the face and palms, macular pigmentary changes on the palms and soles, hyperpigmentation overlying the superficial venous network also called serpentine supravenous hyperpigmentation (SSH) and persistent supravenous erythematous eruptions (PSEE).(2) Keywords: Serpentine Supra-venous Hyperpigmentation, Dermatological toxicity, Fluorouracil.

Leigh's disease, a fatal finding in the common world: A case report.

Leigh syndrome is a neurodegenerative mitochondrial disorder of childhood characterized by symmetrical spongiform lesions in the brain. The clinical presentation of Leigh's syndrome can vary significantly. However, in the majority of cases, it usually presents as a progressive neurological disease involving motor and cognitive development. It is common to see signs and symptoms of the midbrain and brainstem involvement. Limited data are present on the brain processes occurring in Leigh's syndrome which can be attributed to fatal respiratory failure. Raised lactate levels in the blood and/or cerebrospinal fluid are noted. Magnetic resonance imaging (MRI) findings such as necrotic, symmetrical lesions in the BG/brain stem are helpful in arriving at the diagnosis of Leigh's syndrome. It's of utmost importance to determine whether fatal respiratory failure can be predicted based on clinical characteristics and findings on MRI. In our report, we presented 3 cases from rural India, including a 2-year-old male child presenting with UMN lesion signs, a 3-month-old female infant with delayed developmental milestones with lab results suggestive of Leigh's disease, and a 12-year-old female child with epistaxis and generalized weakness. As discussed above, all 3 cases presented differently with a variety of signs and symptoms and would have gone undiagnosed without the use of brain imaging. The study concluded with the impression that while MRI is essential to the initial diagnosis of Leigh's disease, MRI alone cannot be used to predict fatal respiratory failure in patients with Leigh's disease. In any dilemma regarding diagnosis even with MRI, molecular studies remain the gold standard.

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System.

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.