High-risk HPV Prevalence Estimates among Older Patients: Implications for Cervical Cancer Screening Programs.

Background: Due to the heterogeneity of existing studies and wide range of human papilloma virus (HPV) prevalence in India, further research into the incidence of HR-HPV and its spectrum of genotypes is essential to develop screening policies. This study aimed to determine the incidence and demographic distribution of HR-HPV among cisgender female patients attending a tertiary care facility in North India. Materials and Methods: This study was conducted in the Department of Obstetrics and Gynaecology, SGRR Institute of Medical and Health Sciences, Dehradun, India. HPV-DNA test results of 653 female patients were assessed for HR-HPV positivity, genotyping, and age-based differences via Chi-square analysis. Results: Overall prevalence of HR-HPV was 4.90%, HPV-16 was 1.37%, HPV-18 was 0.76%, and HPV non-16,18 was 2.7%. In patients ≤ 50 years, prevalence of HPV-16 was 0.97%, HPV-18 was 0.38%, and HR-HPV non-16,18 was 2.71%. In patients > 50 years, prevalence of HPV-16 was 2.89%, HPV-18 was 2.17%, and HR-HPV non-16,18 was 2.89%. The difference in the prevalence of HPV-16,18 between patients ≤ and > 50 years was found to be highly statistically significant (P = 0.007485). The difference in the prevalence of total HR-HPV between patients ≤ and > 50 years was not found to be statistically significant (P = 0.059905). Conclusion: Our study's finding of higher HR-HPV positivity rates in patients > 50 years emphasizes the need for continued HR-HPV-DNA-based screening of this cohort. With widespread use in post-menopausal patients, HPV screening can serve as an important armamentarium in the fight against cervical cancer.

Machine learning empowered next generation DNA sequencing: perspective and prospectus.

The pursuit of ultra-rapid, cost-effective, and accurate DNA sequencing is a highly sought after aspect of personalized medicine development. With recent advancements, mainstream machine learning (ML) algorithms hold immense promise for high throughput DNA sequencing at the single nucleotide level. While ML has revolutionized multiple domains of nanoscience and nanotechnology, its implementation in DNA sequencing is still in its preliminary stages. ML-aided DNA sequencing is especially appealing, as ML has the potential to decipher complex patterns and extract knowledge from complex datasets. Herein, we present a holistic framework of ML-aided next-generation DNA sequencing with domain knowledge to set directions toward the development of artificially intelligent DNA sequencers. This perspective focuses on the current state-of-the-art ML-aided DNA sequencing, exploring the opportunities as well as the future challenges in this field. In addition, we provide our personal viewpoints on the critical issues that require attention in the context of ML-aided DNA sequencing.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling.

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach. The ML tools achieve classification accuracy as high as 100% in decoding RNA and 98% for decoding both RNA and RNA modifications simultaneously. The relationships between input features and target values have been well examined through Shapley additive explanations. Furthermore, transmission and sensitivity readouts enable the recognition of RNA and its modifications with good selectivity and sensitivity. Our approach represents a starting point for ML-assisted direct RNA sequencing that can potentially decode RNA and its epigenetic modifications at the molecular level.

Effect of myofascial release and muscle energy technique on patients with chronic neck pain: a scoping review.

PURPOSE: To identify the effects of muscle energy techniques and myofascial release in patients with chronic neck pain. METHODS: To conduct a literature search and identification; PRISMA-ScR guidelines were followed. Relevant articles were searched for from the following medical and health sciences electronic databases: PubMed, EBSCOhost, CENTRAL of the Cochrane Library, and the Physiotherapy Evidence-Based Database (PEDro). Patients with chronic neck pain were eligible for the scoping review. RESULTS: Seven articles were included in this review. This scoping review found that there is heterogeneity in the prescription of MFR and MET and a greater tendency to check three major physical dimensions (pain, range of motion, and disability). Various studies have opted for distinct intervention regimens, resulting in disparities in the frequency of weekly interventions, which can range from biweekly to five times a week. These inconsistencies may lead to perplexity among practitioners, as each intervention modality demonstrates favorable outcomes for individuals with persistent cervical discomfort. Moreover, a significant proportion of research projects have employed the numeric pain rating scale (NPRS) and visual analog scale (VAS) for data quantification. CONCLUSION: According to results, majority of the studies were focused on pain and missing components of range of motion and quality of life. Work-related factors can act as risk factors for chronic neck pain. Future investigations should adopt a comprehensive methodology and incorporate QoL assessments of quality of life.

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap.

DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.

Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods.

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

Deciphering DNA nucleotide sequences and their rotation dynamics with interpretable machine learning integrated C3N nanopores.

A solid-state nanopore combined with the quantum transport method has garnered substantial attention and intrigue for DNA sequencing due to its potential for providing rapid and accurate sequencing results, which could have numerous applications in disease diagnosis and personalized medicine. However, the intricate and multifaceted nature of the experimental protocol poses a formidable challenge in attaining precise single nucleotide analysis. Here, we report a machine learning (ML) framework combined with the quantum transport method to accelerate high-throughput single nucleotide recognition with C3N nanopores. The optimized eXtreme Gradient Boosting Regression (XGBR) algorithm has predicted the fingerprint transmission of each unknown nucleotide and their rotation dynamics with root mean square error scores as low as 0.07. Interpretability of ML black box models with the game theory-based SHapley Additive exPlanation method has provided a quasi-explanation for the model working principle and the complex relationship between electrode-nucleotide coupling and transmission. Moreover, a comprehensive ML classification of nucleotides based on binary, ternary, and quaternary combinations shows maximum accuracy and F1 scores of 100%. The results suggest that ML in tandem with a nanopore device can potentially alleviate the experimental hurdles associated with quantum tunneling and facilitate fast and high-precision DNA sequencing.

Quasi-Isomeric Anion-Templated Silver Nanoclusters: Effect of Bulkiness on Luminescence.

Anion-templated silver nanoclusters are fascinating to study because of their diverse structures, which are dictated by the nature of both anions and ligands. Here, we used the bulky 1-ethynyladamantane as one of the protecting ligands alongside trifluoracetate to successfully synthesize a chlorine-templated silver nanocluster─Cl@Ag19(C12H15)11(C2O2F3)7. Elucidation of its structure by single crystal X-ray diffraction revealed the structure to be a chlorine-centered Ag19 cage with protection by alkynyl and carboxylic ligands. This cluster is non-emissive at room temperature and showed green emission with a large Stokes shift at low temperature. The crystal structure was found to be quasi-isomeric with a previously reported Ag19 cluster protected by tert-butyl acetylene, which is emissive at room temperature. Detailed photoluminescence studies and structure-property correlation revealed that the arrangement of the silver skeleton which is influenced by the bulky substituent of the ligand might be responsible for the difference in emission properties.

Protein Sequencing with Artificial Intelligence: Machine Learning Integrated Phosphorene Nanoslit.

Achieving high throughput protein sequencing at single molecule resolution remains a daunting challenge. Herein, relying on a solid-state 2D phosphorene nanoslit device, an extraordinary biosensor to rapidly identify the key signatures of all twenty amino acids using an interpretable machine learning (ML) model is reported. The XGBoost regression algorithm allows the determination of the transmission function of all twenty amino acids with high accuracy. The resultant ML and DFT studies reveal that it is possible to identify individual amino acids through transmission and current signals readouts with high sensitivity and selectivity. Moreover, we thoroughly compared our results to those from graphene nanoslit and found that the phosphorene nanoslit device can be an ideal candidate for protein sequencing up to a 20-fold increase in transmission sensitivity. The present study facilitates high throughput screening of all twenty amino acids and can be further extended to other biomolecules for disease diagnosis and therapeutic decision making.

Adverse Events Following Immunization Among Children Under Two Years of Age: A Prospective Observational Study From North India.

BACKGROUND: Vaccination is one of the most cost-effective child survival health interventions. A single serious adverse event following immunization (AEFI) or a cluster of events may lead to a loss of public confidence in the program and a major setback to immunization coverage. This study was conducted to assess the incidence of AEFIs in children less than two years of age. MATERIAL AND METHODS: A prospective community-based observational study was conducted in the North Indian state of Uttarakhand from October 2017 to February 2018. A total of 255 children who attended the selected sub-centres for immunization were finally included in the study. Follow-up home visits on the 8th and 30th day of vaccination were carried out to identify any occurrences of AEFIs. RESULTS: Among 255 children, 212 AEFIs from 152 vaccinated subjects were reported. The majority of the AEFIs were reported in the age group 0-1 years. The incidence of AEFIs was 33.0 per 100 doses of vaccines administered. The most common AEFI reported was fever (101, 47.6%), followed by swelling (53, 25.0%). Among the vaccines, Pentavalent + oral polio vaccine (OPV) (48.8 per 100 doses) was majorly responsible for AEFIs, followed by diphtheria pertussis tetanus (DPT) + measles and rubella (MR) + OPV (46.6 per 100 doses). CONCLUSION: Our findings revealed that although the incidence of AEFI reported was high, all of them were minor and no serious AEFIs were identified. The awareness among health professionals and the public regarding the reporting of AEFIs should be continued to increase the safety profile of vaccines.

A Step toward Amino Acid-Labeled DNA Sequencing: Boosting Transmission Sensitivity of Graphene Nanogap.

Existing obstacles in next-generation DNA sequencing techniques, for instance, high noise, high translocation speed, and configurational fluctuations, call for approaches capable of reaching the goal and accelerating the process of personalized medicine development. The labeling nucleotide approach has the potential to overcome these barriers and boost the recognition sensitivity of a solid-state nanodevice. In this theoretical report, the first-principles density functional theory calculations have been employed to study the role of three different labels, tyrosine (Tyr), aspartic acid (Asp), and arginine (Arg), for labeling DNA nucleotides and study their effect in rapid and controlled DNA sequencing at atomic resolution. Remarkable differences in interaction energy values are noticed in all three cases of differently labeled nucleotides. The zero-bias transmission spectra confirm that proposed labels have the ability to detect the individual nucleotide, amplifying the tunneling current sensitivity by several orders of magnitude. The current-voltage characteristics of Arg-labeled nucleotides are found to be promising for single nucleotide recognition even at a very low bias voltage of 0.1 V.

Towards a graphene semi/hybrid-nanogap: a new architecture for ultrafast DNA sequencing.

The tremendous upsurge in the research of next-generation sequencing (NGS) methods has broadly been driven by the rise of the wonder material graphene and continues to dominate the futuristic approaches for fast and accurate DNA sequencing. The success of graphene has also triggered the search for many new potential NGS methods capable of ultrafast, reliable, and controlled DNA sequencing. The present study delves into the potential of a new NGS architecture utilizing graphene, namely, a 'semi/hybrid-nanogap' for the identification of DNA nucleobases with single-base resolution. In the framework of first-principles density functional theory methods, we have calculated the transmission function and current-voltage (I-V) characteristics which are of particular significance for DNA sequencing applications. It is noted that the interaction energy values are significantly reduced compared to the previously reported graphene nanodevices, which can lead to a controlled translocation during experimental measurements. Based on the transmission function, each nucleobase can be identified with pertinent sensitivity. It is noticed that the use of highly conductive nucleobase analogs can facilitate improved single nucleobase sensing by increasing the transmission sensitivity. Therefore, we believe that the present study opens up promising frontiers for sequencing applications.

Patient Perspectives on Contraceptive use in North India: A Case for Increased Contraceptive Counseling by Providers.

Background: Given the underutilization of contraception in India, this study was undertaken to gauge cisgender female clients' knowledge of, attitudes toward, and barriers to contraceptive usage in North India. Methodology: The present study was done at a tertiary care Institute in North India, where 209 structured interviews were conducted with cisgender female patients attending the outpatient department. One-way chi-square tests for independence, Kruskal-Wallis test, and Wilcoxon test were applied to quantitative data. Themes from qualitative questions were coded and analyzed. Results: Differences in awareness among contraceptives were found to be highly statistically significant (H (9) = 1022.3, p < 2.2 e-16). Friends or colleagues comprised the predominant information source for most contraceptive methods. Participants' contraceptive usage was low, with 27.27% stating no prior use and 47.47% indicating occasional use (X2 (3, N = 198) = 66.121, p < 2.89 e-14). Lack of perceived need, concern for side effects, fear and desire for children were top reasons for non-use of contraceptive methods. Majority of the participants (79.45%) expressed comfort speaking with their spouse about contraception, 47.18% with a medical provider, 32.82% with friends, 15.38% with family, 2.05% with a health educator, and 3.59% with no one. Participants indicated little prior contraceptive counseling experience. Conclusion: Our study shows differential levels of awareness, usage, and barriers on contraceptive methods among participants. Results also suggest the importance of spouses and friends in clients' contraceptive decision-making process and their limited counseling experience with health care providers.

Machine Learning Prediction of the Transmission Function for Protein Sequencing with Graphene Nanoslit.

Protein sequencing has rapidly changed the landscape of healthcare and life science by accelerating the growth of diagnostics and personalized medicines for a variety of fatal diseases. Next-generation nanopore/nanoslit sequencing is promising to achieve single-molecule resolution with chromosome-size-long readability. However, due to inherent complexity, high-throughput sequencing of all 20 amino acids demands different approaches. Aiming to accelerate the detection of amino acids, a general machine learning (ML) method has been developed for quick and accurate prediction of the transmission function for amino acid sequencing. Among the utilized ML models, the XGBoost regression model is found to be the most effective algorithm for fast prediction of the transmission function with a very low test root-mean-square error (RMSE ∼0.05). In addition, using the random forest ML classification technique, we are able to classify the neutral amino acids with a prediction accuracy of 100%. Therefore, our approach is an initiative for the prediction of the transmission function through ML and can provide a platform for the quick identification of amino acids with high accuracy.

Development of multigrain chapatti with spices mix to enhance the nutritional values and their storage study.

The present investigation was carried out to study the effect of various levels of various multigrain viz., finger millet, pearl millet and fenugreek powder on chapatti (Multigrain chapatti with spices). The multigrain powders were blended in whole-wheat flour along with spices and chapatti flour mixes were prepared. Chapatti composite flour was evaluated for proximate analysis, colour, rheological (viz, amylographic and farinographic) properties and compared with control wheat flour chapatti. Farinograph properties showed that in general dough development time increased in the composite flours. The pasting temperature, peak viscosity, hot paste viscosity, cold paste viscosity, breakdown, setback values were influenced by the addition of other grain flour to wheat flour. The chapatti was evaluated for proximate composition viz. moisture, ash, alcoholic acidity, protein, fat, dietary fiber, carbohydrate, calorific values; sensory analysis, colour determination, texture and microbial analysis. Chapatti prepared with composite flour with finger millet, pearl millet and fenugreek powder were found to be superior over the control chapatti sample. Storage studies of chapatti were carried out for a period of one month at room temperature 25 ± 2 °C and freezer at 4 °C and were found to be suitable for consumption and palatable with desirable characteristics of sensory, texture, appearance, colour and aroma.

Knowledge about tuberculosis among pulmonary tuberculosis patients: A cross-sectional study from Uttarakhand.

BACKGROUND: Tuberculosis (TB) is a major health problem in India. The Revised National TB Control Programme (RNTCP) is working towards elimination of TB in the country by 2025. As the RNTCP relies on passive case finding, it is crucial for the success of the RNTCP that TB patients have knowledge about their disease. The present study aimed to assess the knowledge of TB among pulmonary TB (PTB) patients. MATERIALS AND METHODS: A cross-sectional questionnaire based study using a pretested semi-structured questionnaire among new and previously treated PTB patients at Haldwani Block of Nainital District of Uttarakhand State of North India. Data was analyzed using the software Epi Info version 7.2.0.1. RESULTS: A total of 111 PTB patients with mean age of 36.3 years were included for final analysis. Only 43.2% PTB patients were aware that TB is caused by germs, 48.6% knew that it is not a hereditary disease. Only 13.5% PTB patients knew that vaccine is available and majority (68.5%) were aware of covering mouth and nose while coughing and sneezing for prevention of the disease. Overall, only two-third (65%) patients had good knowledge about TB. CONCLUSIONS: About one-third of PTB patients had poor knowledge about TB. This highlights that to achieve elimination of TB, RNTCP needs to change the present information, education, and communication (IEC) system which is based on a bio-medical framework, and to design a culturally sensitive health education system. Alternatively, the Programme needs to shift from passive case finding to active case finding strategy.