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.

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 [...].

EGFR detection by liquid biopsy: ripe for clinical usage.

Introduction: With the International Association for the Study of Lung Cancer (IASLC) recommendations promoting liquid biopsy as a primary detection tool, a new era of research has begun. The authors aimed to study the concordance of plasma genotyping platforms against the tissue gold standard. Methods: 184 patients with non-small cell lung cancer underwent EGFR genotyping using Cobas, droplet digital polymerase chain reaction (ddPCR) and Therascreen assays from 2019-2020. Results: Of 184 cases, 70 were positive by Cobas, 51 by ddPCR and 69 by Therascreen. The sensitivity of Cobas was 97.1% and the sensitivity of ddPCR was 71%. Receiver operating characteristic analysis showed an area under the curve of 0.977 for Cobas and 0.846 for ddPCR. Conclusion: In line with the FLAURA trial of osimertinib making its way to first-line and given the IASLC recommendations, it is important to understand the attributes of these tests to initiate appropriate treatment.

Lay abstract Lung cancer is one of the most common malignancies and has been known to have a dismal outcome. However, owing to evolution in the knowledge of disease biology and processes, many molecules have been discovered that can be used in targeted therapy. To institute this modality of treatment, detection of alterations in these specific molecules, namely: EGFR, ALK, ROS1, RET, MET, KRAS G12C, BRAF V600E, NTRK1, NTRK2, NTRK3 and ERBB2 is necessary. This has traditionally been done using single-gene assays, which require more tissue. This is a major limitation in cases of non-small cell lung carcinoma, as the biopsies are small. Hence, new technologies like next-generation sequencing have emerged that offer a one-stop solution for these cases. In cases where tissue is very scant, the use of peripheral blood has now been recommended by international guidelines for primary detection of these molecular alterations. This article describes the concordance of tissue-based detection and blood-based detection using three different assays, for the detection of EGFR alterations. Although promising results were obtained largely for blood-based assays, liquid and tissue biopsies are complementary.

Engineered 2D materials for optical bioimaging and path toward therapy and tissue engineering.

Two-dimensional (2D) layered materials as a new class of nanomaterial are characterized by a list of exotic properties. These layered materials are investigated widely in several biomedical applications. A comprehensive understanding of the state-of-the-art developments of 2D materials designed for multiple nanoplatforms will aid researchers in various fields to broaden the scope of biomedical applications. Here, we review the advances in 2D material-based biomedical applications. First, we introduce the classification and properties of 2D materials. Next, we summarize surface and structural engineering methods of 2D materials where we discuss surface functionalization, defect, and strain engineering, and creating heterostructures based on layered materials for biomedical applications. After that, we discuss different biomedical applications. Then, we briefly introduced the emerging role of machine learning (ML) as a technological advancement to boost biomedical platforms. Finally, the current challenges, opportunities, and prospects on 2D materials in biomedical applications are discussed.

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.

Adjustment of photosynthetic activity to drought and fluctuating light in wheat.

Drought is a major cause of losses in crop yield. Under field conditions, plants exposed to drought are usually also experiencing rapid changes in light intensity. Accordingly, plants need to acclimate to both, drought and light stress. Two crucial mechanisms in plant acclimation to changes in light conditions comprise thylakoid protein phosphorylation and dissipation of light energy as heat by non-photochemical quenching (NPQ). Here, we analyzed the acclimation efficacy of two different wheat varieties, by applying fluctuating light for analysis of plants, which had been subjected to a slowly developing drought stress as it usually occurs in the field. This novel approach allowed us to distinguish four drought phases, which are critical for grain yield, and to discover acclimatory responses which are independent of photodamage. In short-term, under fluctuating light, the slowdown of NPQ relaxation adjusts the photosynthetic activity to the reduced metabolic capacity. In long-term, the photosynthetic machinery acquires a drought-specific configuration by changing the PSII-LHCII phosphorylation pattern together with protein stoichiometry. Therefore, the fine-tuning of NPQ relaxation and PSII-LHCII phosphorylation pattern represent promising traits for future crop breeding strategies.

Macrophage HIF-2α regulates tumor-suppressive Spint1 in the tumor microenvironment.

In solid tumors, tumor-associated macrophages (TAMs) commonly accumulate within hypoxic areas. Adaptations to such environments evoke transcriptional changes by the hypoxia-inducible factors (HIFs). While HIF-1α is ubiquitously expressed, HIF-2α appears tissue-specific with consequences of HIF-2α expression in TAMs only being poorly characterized. An E0771 allograft breast tumor model revealed faster tumor growth in myeloid HIF-2α knockout (HIF-2αLysM-/- ) compared with wildtype (wt) mice. In an RNA-sequencing approach of FACS sorted wt and HIF-2α LysM-/- TAMs, serine protease inhibitor, Kunitz type-1 ( Spint1) emerged as a promising candidate for HIF-2α-dependent regulation. We validated reduced Spint1 messenger RNA expression and concomitant Spint1 protein secretion under hypoxia in HIF-2α-deficient bone marrow-derived macrophages (BMDMs) compared with wt BMDMs. In line with the physiological function of Spint1 as an inhibitor of hepatocyte growth factor (HGF) activation, supernatants of hypoxic HIF-2α knockout BMDMs, not containing Spint1, were able to release proliferative properties of inactive pro-HGF on breast tumor cells. In contrast, hypoxic wt BMDM supernatants containing abundant Spint1 amounts failed to do so. We propose that Spint1 contributes to the tumor-suppressive function of HIF-2α in TAMs in breast tumor development.

An evolutionary perspective of AMPK-TOR signaling in the three domains of life.

AMPK and TOR protein kinases are the major control points of energy signaling in eukaryotic cells and organisms. They form the core of a complex regulatory network to co-ordinate metabolic activities in the cytosol with those in the mitochondria and plastids. Despite its relevance, it is still unclear when and how this regulatory pathway was formed during evolution, and to what extent its representations in the major eukaryotic lineages resemble each other. Here we have traced 153 essential proteins forming the human AMPK-TOR pathways across 412 species representing all three domains of life-prokaryotes (bacteria, archaea) and eukaryotes-and reconstructed their evolutionary history. The resulting phylogenetic profiles indicate the presence of primordial core pathways including seven proto-kinases in the last eukaryotic common ancestor. The evolutionary origins of the oldest components of the AMPK pathway, however, extend into the pre-eukaryotic era, and descendants of these ancient proteins can still be found in contemporary prokaryotes. The TOR complex in turn appears as a eukaryotic invention, possibly to aid in retrograde signaling between the mitochondria and the remainder of the cell. Within the eukaryotes, AMPK/TOR showed both a highly conserved core structure and a considerable plasticity. Most notably, KING1, a protein originally assigned as the γ subunit of AMPK in plants, is more closely related to the yeast SDS23 gene family than to the γ subunits in animals or fungi. This suggests its functional difference from a canonical AMPK γ subunit.

An evolutionary view on thylakoid protein phosphorylation uncovers novel phosphorylation hotspots with potential functional implications.

The regulation of photosynthetic light reactions by reversible protein phosphorylation is well established today, but functional studies have so far mostly been restricted to processes affecting light-harvesting complex II and the core proteins of photosystem II. Virtually no functional data are available on regulatory effects at the other photosynthetic complexes despite the identification of multiple phosphorylation sites. Therefore we summarize the available data from 50 published phospho-proteomics studies covering the main complexes involved in photosynthetic light reactions in the 'green lineage' (i.e. green algae and land plants) as well as its cyanobacterial counterparts. In addition, we performed an extensive orthologue search for the major photosynthetic thylakoid proteins in 41 sequenced genomes and generated sequence alignments to survey the phylogenetic distribution of phosphorylation sites and their evolutionary conservation from green algae to higher plants. We observed a number of uncharacterized phosphorylation hotspots at photosystem I and the ATP synthase with potential functional relevance as well as an unexpected divergence of phosphosites. Although technical limitations might account for a number of those differences, we think that many of these phosphosites have important functions. This is particularly important for mono- and dicot plants, where these sites might be involved in regulatory processes such as stress acclimation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.