Experiences of newly diagnosed oral cancer patients during the first wave of the COVID-19 pandemic: A qualitative study from Pakistan

The COVID-19 pandemic has resulted in the scaling back or postponement of non-emergency hospital services, including care of cancer patients. The present qualitative study explored the experiences of newly diagnosed oral cancer patients during the first wave of the COVID-19 pandemic in Pakistan. Patients who attended the Department of Maxillofacial Surgery, Khyber College of Dentistry in July 2020 were selected using a maximum variation purposive sampling method. Seventeen semi-structured interviews were conducted in Pashto, the local language of Khyber Pakhtunkhwa. All interviews were audiotaped, transcribed verbatim, and translated into English. Thematic content analysis yielded eight major themes: pain and generalised physical weakness, shock at diagnosis, psychological distress of the COVID-19 pandemic, faith and religion, double hit loss of employment, social isolation, social support from caregivers, and lack of support from health care professionals. In conclusion, the COVID-19 pandemic has a clear impact on the life experiences of newly diagnosed oral cancer patients. Distress due to delay in accessing health care and lack of support from health care providers are a matter of great concern. Appropriate interventions should be introduced to ensure psychological and social support strategies are in place for patients during interruptions of health care services

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions

Development of a Sensor to Detect Condensation of Super-Sonic Steam

This paper explains the development and functioning of AC driven electrodes based sensor which is used for the study of condensation phenomena of steam. Time for the AC signals starts form 20 msecond to 1 second. Data acquisition system is employed against each time interval and the output data is fed into EIDORS (a free software algorithm). Images show the clear boundaries between pure steam, its interface and water

In vitro inter-relationship between plant growth promoting rhizobacteria and root knot nematode (Meloidogyne incognita) and their effect on growth parameters of brinjal

The influence of rhizobacteria as the treatment on germination, migration and penetration of Meloidogyne incognita in brinjal was evaluated under laboratory conditions. The results obtained were highly significant and revealed that Pseudomonas fluorescens promotes germination 87.5% and was effective in reducing root penetration by Meloidogyne incognita i.e. 39.3 juviniles. Due to the effect of P. fluorescens, the plant height increased by 40.9%, number of leaves was maximum i.e., 50%, number of gall formation was also controlled i.e., 70.3%. It was concluded from the studies that rhizobacterium Pseudomonas fluorescens is a potential biocontol agent and it has ability to increase the yield and suppress the attack of plant pathogen

Photocatalytic Z-Scheme Overall Water Splitting: Recent Advances in Theory and Experiments

Photocatalytic water splitting is considered one of the most important and appealing approaches for the production of green H2 to address the global energy demand. The utmost possible form of artificial photosynthesis is a two-step photoexcitation known as “Z-scheme”, which mimics the natural photosystem. This process solely relies on the effective coupling and suitable band positions of semiconductors (SCs) and redox mediators for the purpose to catalyze the surface chemical reactions and significantly deter the backward reaction. In recent years, the Z-scheme strategies and their key role have been studied progressively through experimental approaches. In addition, theoretical studies based on density functional theory have provided detailed insight into the mechanistic aspects of some breathtakingly complex problems associated with hydrogen evolution reaction and oxygen evolution reaction. In this context, this critical review gives an overview of the fundamentals of Z-scheme photocatalysis, including both theoretical and experimental advancements in the field of photocatalytic water splitting, and suggests future perspectives

A comparative study to evaluate the effects of antibiotics, plant extracts and fluoride-based toothpaste on the oral pathogens isolated from patients with gum diseases in Pakistan

Oral diseases caused by various microorganisms are common around the world. Scientific research has now been focusing on novel medicines to overcome bacterial resistance and antibiotics side effects; therefore, the current study was designed to assess the efficacy of certain antibiotics, toothpaste, and medicinal plant extracts (Ajuga bracteosa and Curcuma longa) versus the bacterial pathogens isolated from the human oral cavity. A total of 130 samples were collected from Khyber Teaching Hospital Peshawar, Pakistan, among those 27 species isolated, and eight bacterial species were identified from the samples. Among all the bacterial species, Staphylococcus aureus (29.62%) and Proteus mirabilis (22.2%) were found to be more prevalent oral pathogens. In comparison, the least pervasive microbes were Proteus vulgaris, Shigella sonnei, Escherichia coli and Aeromonas hydrophila. The study also suggested that dental problems were more prevalent in males (41-50 years of age) than females. Among the eight antibiotics used in the study, the most promising results were shown by Foxicillin against A. hydrophila. The survey of TP1 revealed that it showed more potent antagonist activity against Proteus vulgaris as compared TP2 and TP3 that might be due to the high content of fluoride. The Curcuma longa showed more significant activity than Ajuga bracteosa (Stem, leaves and root) extracts. The data obtained through this study revealed that antibiotics were more effective for oral bacterial pathogens than toothpaste and plant extracts which showed moderate and low activity, respectively. Therefore, it is suggested that the active compounds in individual medicinal plants like Curcuma longa and Ajuga bracteosa could replace the antibiotics when used in daily routine as tooth cleansers or mouth rinses

Cognitive therapy for depression in tuberculosis treatment: protocol for multicentre pragmatic parallel arm randomised control trial with an internal pilot

Introduction and objectives There is an unmet need to develop high-quality evidence addressing tuberculosis (TB)-related mental health comorbidity, particularly in the context of lower-middle-income countries. This study aims to examine the effectiveness and cost-effectiveness of cognitive behavioural therapy (CBT) versus enhanced treatment as usual (ETAU) in improving depressive symptoms in people with TB and comorbid depression, enhancing adherence with anti-TB treatment (ATT) and its implementation in the real-world setting of Pakistan. Methods We will conduct a pragmatic parallel arm randomised control trial with an internal pilot. A brief psychological intervention based on CBT has been developed using a combination of qualitative and ethnographic studies. The inbuilt pilot trial will have a sample size of 80, while we plan to recruit 560 (280 per arm) participants in the definitive trial. Participants who started on ATT within 1 month of diagnosis for pulmonary and extrapulmonary TB or multidrug resistant TB (MDR-TB) and meeting the criteria for depression on Patient Health Questionnaire-9 (PHQ-9) will be randomised with 1:1 allocation to receive six sessions of CBT (delivered by TB healthcare workers) or ETAU. Data on the feasibility outcomes of the pilot will be considered to proceed with the definitive trial. Participants will be assessed (by a blinded assessor) for the following main trial primary outcomes: (1) severity of depression using PHQ-9 scale (interviewer-administered questionnaire) at baseline, weeks 8, 24 and 32 postrandomisation and (2) ATT at baseline and week 24 at the end of ATT therapy. Ethics and dissemination Ethical approval has been obtained from Keele University Research Ethics Committee (ref: 2023-0599-792), Khyber Medical University Ethical Review Board (ref: DIR/KMU-EB/CT/000990) and National Bioethics Committee Pakistan (ref: No.4–87/NBC-998/23/587). The results of this study will be reported in peer-reviewed journals and academic conferences and disseminated to stakeholders and policymakers. Trial registration number ISRCTN10761003

Diagnosis of enteric fever in the emergency department: a retrospective study from Pakistan

Background:Enteric fever is one of the top differential diagnoses of fever in many parts of the world. Generally, the diagnosis is suspected and treatment is initiated based on clinical and basic laboratory parameters.Aims: The present study identifies the clinical and laboratory parameters predicting enteric fever in Patients visiting the emergency department of a tertiary care hospital in Pakistan.Methods:This is a retrospective chart review of all adult Patients with clinically suspected enteric fever admitted to the hospital through the emergency department during a 5-year period (2000-2005).Results:A total of 421 emergency department Patients were admitted to the hospital with suspected enteric fever. There were 53 cases of blood culture-positive enteric fever and 296 disease-negative cases on culture. The mean age in the blood culture-positive group was 27 years (SD: 10) and in the group with negative blood culture for enteric fever, 35 years (SD: 15) with a male to female ratio of 1:0.6 in both groups. Less than half (48%) of all Patients admitted with suspected enteric fever had the discharge diagnosis of enteric fever, of which only 13% of the Patients had blood culture/serologically confirmed enteric fever. None of the common clinical and laboratory parameters differed between enteric fever-positive Patients and those without it.Conclusion:Commonly cited clinical and laboratory parameters were not able to predict enteric fever

Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant

Facile synthesis of high-quality Nano-size 10B-enriched fibers of hexagonal boron nitride

The interesting properties of hexagonal boron nitride (h-BN) and its potential uses in thermo-structural advanced applications have been limited or restricted by its inherent brittleness, which can easily be eliminated by its fibers (h-BN) in nanoscale dimensions. The current study is based on the synthesis of nanoscale 10B-enriched fibers of h-BN (10BNNFs) from 10B in the precursors instead of B in two-hour annealing at 900 °C and one-hour growth at 1000 °C. All of the 10BNNFs are randomly curved and highly condensed or filled from 10h-BN species with no internal space or crack. XRD peaks reported the 10h-BN phase and highly crystalline nature of the synthesized 10BNNFs. 10h-BN phase and crystalline nature of 10BNNFs are confirmed from high-intensity peaks at 1392 (cm−1) in Raman and FTIR spectroscope