A systematic review on deep learning-based automated cancer diagnosis models.

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

Statin Usage in Peripheral Arterial Disease Patients.

OBJECTIVES: Atherosclerotic cardiovascular disease (ASCVD) events have been shown to occur at higher frequency in patients with peripheral arterial disease (PAD). In this study, our aim is to evaluate whether statin is being used appropriately in patients with PAD and also evaluate its usage with the number of vascular beds involved. MATERIALS AND METHODS: This retrospective cross-sectional study reviewed data of patients with a confirmed diagnosis of PAD based on invasive or noninvasive imaging. Demographic, clinical, laboratory, and treatment data collected were described using descriptive statistics. Multiple logistic regression analysis was conducted to determine the predictors for the prescription of statins (HIS). High-intensity statin therapy was defined as atorvastatin ≥40 mg per day, rosuvastatin ≥20 mg per day, or simvastatin ≥80 mg per day, according to American College of Cardiology (ACC)/American Heart Association (AHA) guidelines. RESULTS: We analyzed data from 166 patients who met the inclusion criteria. The mean age was 63.34 years. The most common comorbidity was diabetes mellitus (DM) (68.86%). Statins were used in 82% of patients, among whom only 39% were on high-intensity statins. Multiple logistic regression analysis revealed that patients with cerebrovascular disease (CVD) [odds ratio (OR) = 0.19, 95% confidence interval (CI) = 0.06-0.61, p = 0.005], on oral anticoagulants (OAC) (OR = 0.16, 95% CI = 0.04-0.62, p = 0.008) and on dual antiplatelet therapy (DAPT) (OR = 0.20, 95% CI = 0.08-0.47, p < 0.000) had lower odds of receiving lower extremity revascularization (LIS) therapy. CONCLUSION: Despite having a high risk of future adverse cardiac events, patients with PAD are less likely to receive appropriate statin therapy. Involvement of more vascular beds was associated with higher chances of initiating high-intensity statin.

Biotreatment of azo dye containing textile industry effluent by a developed bacterial consortium immobilised on brick pieces in an indigenously designed packed bed biofilm reactor.

This study highlights the development of a lab-scale, indigenously designed; Packed-Bed Biofilm Reactor (PBBR) packed with brick pieces. The developed biofilm in the reactor was used for the decolourisation and biodegradation of the textile industry effluent. The PBBR was continuously operated for 264 days, during which 301 cycles of batch and continuous treatment were operated. In batch mode under optimised conditions, more than 99% dye decolourisation and ≥ 92% COD reduction were achieved in 6 h of contact time upon supplementation of effluent with 0.25 g L-1 glucose, 0.25 g L-1 urea, and 0.1 g L-1 phosphates. A decolourisation rate of 133.94 ADMI units h-1 was achieved in the process. PBBR, when operated in continuous mode, showed ≥ 95% and ≥ 92% reduction in ADMI and COD values. Subsequent aeration and passage through the charcoal reactor assisted in achieving a ≥ 96% reduction in COD and ADMI values. An overall increase of 81% in dye-laden effluent decolourisation rate, from 62 to 262 mg L-1 h-1, was observed upon increasing the flow rate from 18 to 210 mL h-1. Dye biodegradation was determined by UV-Vis and FTIR spectroscopy and toxicity study. SEM analysis showed the morphology of the attached-growth biofilm.

Anti-TNF treatment negatively regulates human CD4+ T-cell activation and maturation in vitro, but does not confer an anergic or suppressive phenotype.

TNF-blockade has shown clear therapeutic value in rheumatoid arthritis and other immune-mediated inflammatory diseases, however its mechanism of action is not fully elucidated. We investigated the effects of TNF-blockade on CD4+ T cell activation, maturation, and proliferation, and assessed whether TNF-inhibitors confer regulatory potential to CD4+ T cells. CyTOF and flow cytometry analysis revealed that in vitro treatment of human CD4+ T cells with the anti-TNF monoclonal antibody adalimumab promoted IL-10 expression in CD4+ T cells, whilst decreasing cellular activation. In line with this, analysis of gene expression profiling datasets of anti-TNF-treated IL-17 or IFN-γ-producing CD4+ T cells revealed changes in multiple pathways associated with cell cycle and proliferation. Kinetics experiments showed that anti-TNF treatment led to delayed, rather than impaired T-cell activation and maturation. Whilst anti-TNF-treated CD4+ T cells displayed some hyporesponsiveness upon restimulation, they did not acquire enhanced capacity to suppress T-cell responses or modulate monocyte phenotype. These cells however displayed a reduced ability to induce IL-6 and IL-8 production by synovial fibroblasts. Together, these data indicate that anti-TNF treatment delays human CD4+ T-cell activation, maturation, and proliferation, and this reduced activation state may impair their ability to activate stromal cells.

Rationalization of photo-detachment spectra of the indenyl anion (C9H7-) from the perspective of vibronic coupling theory.

The nuclear dynamics of the low-lying first four electronic states of the prototypical indenyl radical is investigated based on first principles calculations to rationalize the experimental vibronic structure of the radical. The study is performed following both time-dependent and time-independent quantum-chemistry approaches using a model diabatic Hamiltonian. The construction of model Hamiltonians is based on the fits of the adiabatic energies calculated from the electronic structure method. The analyses of the static and dynamics results of the present study corroborate the experimental findings regarding the shape of the spectrum, vibrational progressions and the lifetime of the excited state. Finally, the present theoretical investigations suggest that the electronic non-adiabatic effect is extremely important for a detailed study of the vibronic structure and the electronic relaxation mechanism of the low-lying electronic states of the indenyl radical.

Hashimoto's Thyroiditis among Patients with Thyroid Disorders Visiting a Tertiary Care Centre.

Introduction: Hashimoto's thyroiditis is a chronic autoimmune lymphocytic thyroiditis characterised by thyroid autoantibodies. Early detection and treatment of this condition help in reducing the morbidity and mortality associated with it. The aim of the study was to find out the prevalence of Hashimoto's thyroiditis among patients with thyroid disorders visiting a tertiary care centre. Methods: A descriptive cross-sectional study was conducted among patients visiting the outpatient department of a tertiary care centre. Data from 14 April 2017 to 13 April 2019 was collected between 30 June 2022 to 15 September 2022 from medical records. Ethical approval was obtained from the Nepal Health Research Council. Hashimoto's thyroiditis was diagnosed based on clinical presentation and positive antibodies to thyroid antigens. Convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval. Results: Among 813 patients with thyroid disorders, 393 (48.33%) (44.89-51.77, 95% Confidence Interval) had Hashimoto's thyroiditis. The manifestation of the spectrum of Hashimoto's thyroiditis were euthyroid in 215 (54.70%), subclinical hypothyroidism in 102 (25.95%), subclinical hyperthyroidism in 23 (5.85%), overt hyperthyroidism in 9 (2.30%) and overt hypothyroidism in 4 (1.02%). Conclusions: The prevalence of Hashimoto's thyroiditis among patients with thyroid disorders was higher than in other studies done in similar settings. Keywords: anti-thyroid peroxidase antibodies; Hashimoto's thyroiditis; thyroid disorders.

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses. METHOD: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα. RESULTS: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity. CONCLUSION: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model.

Convolutional neural network (CNN) models have made tremendous progress in the medical domain in recent years. The application of the CNN model is restricted due to a huge number of redundant and unnecessary parameters. In this paper, the weight and unit pruning strategy are used to reduce the complexity of the CNN model so that it can be used on small devices for the diagnosis of lumbar spondylolisthesis. Experimental results reveal that by removing 90% of network load, the unit pruning strategy outperforms weight pruning while achieving 94.12% accuracy. Thus, only 30% (around 850532 out of 3955102) and 10% (around 251512 out of 3955102) of the parameters from each layer contribute to the outcome during weight and neuron pruning, respectively. The proposed pruned model had achieved higher accuracy as compared to the prior model suggested for lumbar spondylolisthesis diagnosis.

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs.

Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.

The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models.

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3's 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy's (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model's performance on other publicly available datasets, we have generalised our approach on the public platform.

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%).

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm.

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.

Recovering from COVID-19: lessons learnt from an intensive secondary care follow-up service.

In response to the first COVID-19 surge in 2020, secondary care outpatient services were rapidly reconfigured to provide specialist review for disease sequelae. At our institution, comprising hospitals across three sites in London, we initially implemented a COVID-19 follow-up pathway that was in line with expert opinion at the time but more intensive than initial clinical guidelines suggested. We retrospectively evaluated the resource requirements for this service, which supported 526 patients from April 2020 to October 2020. At the 6-week review, 193/403 (47.9%) patients reported persistent breathlessness, 46/336 (13.7%) desaturated on exercise testing, 167/403 (41.4%) were discharged from COVID-19-related secondary care services and 190/403 (47.1%) needed 12-week follow-up. At the 12-week review, 113/309 (36.6%) patients reported persistent breathlessness, 30/266 (11.3%) desaturated on exercise testing and 150/309 (48.5%) were discharged from COVID-19-related secondary care services. Referrals were generated to multiple medical specialties, particularly respiratory subspecialties. Our analysis allowed us to justify rationalising and streamlining provisions for subsequent COVID-19 waves while reassured that opportunities for early intervention were not being missed.

Serum Vitamin D in Patients with Chronic Periodontitis and Healthy Periodontium.

BACKGROUND: Periodontitis is inflammatory disorder resulting in progressive destruction of the periodontal ligament and alveolar bone with pocket formation, recession, or both. Vitamin D has a benefit in bone metabolism and anti-inflammatory activity involving T-cell homeostasis. The objective of this study was to compare the vitamin D level in patient with chronic periodontitis and healthy periodontium and evaluate its correlation. METHODS: A comparative cross-sectional study was done in 80 patients (40 with healthy periodontium and 40 with chronic periodontitis) visiting to Department of Dental Surgery (Periodontology and Oral Implantology Section) of Bir Hospital. Parameters measured were plaque index, gingival index, clinical attachment level and pocket depth for the evaluation of healthy periodontium and chronic periodontitis. RESULTS: The result showed 16.58±6.65ng/mL and 19.06±11.52ng/mL level of vitamin D in female and male respectively (p-value 0.230) and 16.85±13.30 ng/mL and 19.78±5.87 ng/mL level of vitamin D in healthy and chronic periodontitis groups respectively (p-value 0.209). CONCLUSIONS: There are no differences in the level of serum vitamin D between healthy and chronic periodontitis groups. No association was seen between vitamin D level and chronic periodontitis.

Symptomatic, biochemical and radiographic recovery in patients with COVID-19.

BACKGROUND: The symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described. METHODS: Retrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness. RESULTS: 75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge. CONCLUSIONS: Patients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19.

Author Correction: A Low-Cost Non-explosive Synthesis of Graphene Oxide for Scalable Applications.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

A Low-Cost Non-explosive Synthesis of Graphene Oxide for Scalable Applications.

A low cost, non-explosive process for the synthesis of graphene oxide (GO) is demonstrated. Using suitable choice of reaction parameters including temperature and time, this recipe does not require expensive membranes for filtration of carbonaceous and metallic residues. A pre-cooling protocol is introduced to control the explosive nature of the highly exothermic reactions during the oxidation process. This alleviates the requirement for expensive membranes and completely eliminates the explosive nature of intermediate reaction steps when compared to existing methods. High quality of the synthesized GO is corroborated using a host of characterization techniques including X-ray diffraction, optical spectroscopy, X-ray photoemission spectroscopy and current-voltage characteristics. Simple reduction protocol using ultra-violet light is demonstrated for potential application in the area of photovoltaics. Using different reduction protocols together with the proposed inexpensive method, reduced GO samples with tunable conductance over a wide range of values is demonstrated. Density functional theory is employed to understand the structure of GO. We anticipate that this scalable approach will catalyze large scale applications of GO.

Clinical study on primary open-angle glaucoma with Ashchyotana, Tarpana and oral medication.

INTRODUCTION: Glaucoma is the second leading cause of irreversible blindness worldwide and third leading cause in India. The disease progresses even intraocular pressure (IOP) is well under control; hence, now modern medicine is looking for strategies that are neuroprotective in glaucomatous optic neuropathy (GON) management. AIM: This study aimed to propound the concept of Chakshushya Rasayana and diuretic therapies and also evaluate the neuroprotective and IOP-lowering effects of Ayurvedic line of management in primary open-angle glaucoma (POAG). MATERIALS AND METHODS: In this randomized parallel-group trial, patients having POAG were randomized with equal probability to one of the two treatment groups. Participants were assessed on the basis of subjective parameters such as blurred vision, frequent changes of presbyopic glasses (FCPG), delayed dark adaptation (DDA), visual field defect (VFD) and headache; objective parameters such as best-corrected visual acuity (BCVA), IOP and optic nerve head changes and perimetry findings such as mean deviation (MD) and Glaucoma Hemifield Test. In Group A, after Koshtha Shodhana and Nasya, Tarpana and Ashchyotana with Shigru Pallava Arka were done locally and Punarnavashtaka Kwatha and Gokshuradi Guggulu were given internally for 52 days along with modern antiglaucoma eye drop and in Group B, patients already taking antiglaucoma eye drop were kept under observation for 2 months. RESULTS: Patients in Group A showed better results in blurred vision, FCPG, DDA, VFD, headache, BCVA, IOP and MD. Patients in Group B showed better results in blurred vision and FCPG. A comparison of both groups showed significant results in blurred vision, DDA, VFD, BCVA, IOP and MD. CONCLUSION: The clinical study concludes that Ayurvedic treatment protocol along with antiglaucoma eye drop in Group A patients was found to be more effective in reducing the IOP and controlling the progression of GON along with modern anti-glaucoma eye drop. Early diagnosis and proper management can prevent, arrest, or delay progression of POAG.

Medial subclavicular musculotendinous complex and insulation break: Rare cause of late pacemaker lead malfunction.

Insulation break in a permanent pacemaker lead is a rare long-term complication. We describe an elderly male with a VVIR pacemaker, who presented with an episode of presyncope more than 3 years after the initial implantation procedure, attributed to insulation break possibly caused by lead entrapment in components of the medial subclavicular musculotendinous complex (MSMC) and repeated compressive damage over time during ipsilateral arm movement requiring lead replacement. The differential diagnosis of a clinical presentation when pacing stimuli are present with failure to capture and the role of the MSMC in causing lead damage late after implantation are discussed.