ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

Salivary Analysis for Evidence based Ayurvedic Diagnosis

Ayurveda is called Mother of all medical sciences. It's the oldest therapeutic and medicinal treatment invented in ancient India. Ayurveda or Ayurvedic treatment is bit different from modern medical science. It believes in Nadi Pariksha and many subjective parameters are included to start diagnosis of disease. Whereas modern medical science has different approach of disease diagnosis. It utilizes different tools and testing to diagnose a disease effectively. Saliva analysis is already accepted in modern medical as an important bio-substance, as we see in COVID-19, but not in ayurveda. This paper shows how salivary analysis can act as an evidential proof for diagnosing a disease, in the ayurvedic way. The salivary contents can be analyzed use various biosensors. One of these is Surface Enhanced Raman Spectroscopy (SERS) platform. It allows molecular detection in bio fluids like saliva, sweat, urine, etc. The saliva analysis using SERS technique will help to detect various trace level molecules which is likely to assist the Ayurvedic diagnosis more accurately and dependency on subjective parameters will reduce to evaluate patient's condition. © 2023 IEEE.

Pilot mental health during the COVID-19 pandemic: prevalence rates from semi-structured interviews, and associated vulnerability and protective factors.

Introduction: Pilots are a unique occupational group who perform a specialised job and face significant stressors. Pilot mental health has received increased attention since Germanwings Flight 9525; however, this research has largely focused on general anxiety, depression, and suicide and relied on a questionnaire-based methodology. This approach is likely to miss various mental health issues that may affect pilot wellbeing, leaving the prevalence of mental health issues in aviation unclear. In addition, the COVID-19 pandemic is likely to have a particular impact on the mental health and wellbeing of pilots, who experienced the devastating effect of COVID-19 on the industry. Method: In the present study, we conducted a comprehensive assessment of 73 commercial pilots during the COVID-19 pandemic, using the DIAMOND semi-structured diagnostic interview and explored possible associated vulnerability and protective factors, including life event stressors, personality, passion, lifestyle factors, and coping strategies. Results: The COVID-19 pandemic had a significant impact on aviation during the time of this study, affecting 95% of participants. The diagnostic results revealed over one third of pilots had symptoms of a diagnoseable mental health disorder. Anxiety disorders were the most commonly found disorders, followed by Attention Deficit Hyperactivity Disorder (ADHD), Adjustment Disorder, and Depressive Disorders. Pilots' high life event scores placed them at an increased risk for the development of stress-related illness, though did not explain which pilots had mental health difficulties in this study. Regression analysis supported a diathesis-stress model for pilot mental health, with disagreeableness and obsessive passion contributing to pilots' development of mental health issues, and nutrition as the most important protective factor. Discussion: This study, though limited to the COVID-19 pandemic, provides a valuable precedent for a more thorough assessment of pilot mental health, and contributes to the broader understanding of pilot mental health, including suggestions to target factors associated with the development of mental health issues.

Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy.

BACKGROUND: A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases. METHODS: We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data. RESULTS: We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate). CONCLUSIONS: We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

A Comparison of Pediatric Mental Health Diagnoses and Selective Serotonin Reuptake Inhibitor Prescribing Before and During the COVID-19 Pandemic.

PURPOSE: To assess the rate of mental health diagnoses and selective serotonin reuptake inhibitor (SSRI) prescribing before and during the Coronavirus Disease 2019 pandemic. METHODS: We conducted a cross-sectional study at an ambulatory pediatric clinic. A prepandemic (June 2018 to June 2019) and intrapandemic (June 2020 to June 2021) cohort were reviewed. The rate of mental health visits and new SSRI prescriptions were compared. Chi-squared analyses demonstrated a variance of statistical significance. RESULTS: From 15,414 encounters (9,791 prepandemic and 5,623 intrapandemic), 397 mental health encounters were identified. 231 (4.1%) encounters occurred during the pandemic (vs. 1.7% prepandemic) and 63 (27.3%) SSRIs were prescribed (vs. 5.4% prepandemic). Mental health encounters (prevalence ratio 2.42, 95% confidence interval, 1.99-2.95, p < .001) and SSRI prescriptions (prevalence ratio 5.03, 95% confidence interval, 2.58-9.82, p < .001) were higher during the pandemic. DISCUSSION: Our findings demonstrate increased rates of SSRI prescribing and mental health diagnoses during the Coronavirus Disease 2019 pandemic, suggesting an increased incidence of these conditions. Clinicians should be prepared to manage and screen for mental health conditions.

Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.

The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, "ChestX-ray14," which involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, and 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 and CoAtNet-0-rw, were chosen as the backbones. After the fully-connected layer, a final layer of 14 sigmoid activation units was added to output each disease's diagnosis. To achieve better adaptive learning, a novel loss (Lours) was designed, which coalesced reweighting and tail sample focus. For comparison, a pretrained ResNet50 network with weighted binary cross-entropy loss (LWBCE) was used as a baseline, which showed the best performance in a previous study. The overall and individual areas under the receiver operating curve (AUROC) for each disease label were evaluated and compared among different models. Group-score-weighted class activation mapping (Group-CAM) is applied for visual interpretations. As a result, the pretrained CoAtNet-0-rw + Lours showed the best overall AUROC of 0.842, significantly higher than ResNet50 + LWBCE (AUROC: 0.811, p = 0.037). Group-CAM presented that the model could pay the proper attention to lesions for most disease labels (e.g., atelectasis, edema, effusion) but wrong attention for the other labels, such as pneumothorax; meanwhile, mislabeling of the dataset was found. Overall, this study presented an advanced AI diagnostic model achieving a significant improvement in the multi-disease diagnosis of chest X-rays, particularly in real-world data with challenging long-tail distributions.

Medical Image Segmentation Based on Transformer and HarDNet Structures

Medical image segmentation is a crucial way to assist doctors in the accurate diagnosis of diseases. However, the accuracy of medical image segmentation needs further improvement due to the problems of many noisy medical images and the high similarity between background and target regions. The current mainstream image segmentation networks, such as TransUnet, have achieved accurate image segmentation. Still, the encoders of such segmentation networks do not consider the local connection between adjacent chunks and lack the interaction of inter-channel information during the upsampling of the decoder. To address the above problems, this paper proposed a dual-encoder image segmentation network, including HarDNet68 and Transformer branch, which can extract the local features and global feature information of the input image, allowing the segmentation network to learn more image information, thus improving the effectiveness and accuracy of medical segmentation. In this paper, to realize the fusion of image feature information of different dimensions in two stages of encoding and decoding, we propose a feature adaptation fusion module to fuse the channel information of multi-level features and realize the information interaction between channels, and then improve the segmentation network accuracy. The experimental results on CVC-ClinicDB, ETIS-Larib, and COVID-19 CT datasets show that the proposed model performs better in four evaluation metrics, Dice, Iou, Prec, and Sens, and achieves better segmentation results in both internal filling and edge prediction of medical images. Accurate medical image segmentation can assist doctors in making a critical diagnosis of cancerous regions in advance, ensure cancer patients receive timely targeted treatment, and improve their survival quality. © 2013 IEEE.

Description of COVID-19 patients and mapping nursing data to ICNP 2021 reference set in SNOMED CT.

AIM: To describe nursing care of COVID-19 patients with International Classification for Nursing Practice (ICNP) 2019, ICNP 2021 reference set, and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). BACKGROUND: From the beginning of the COVID-19 pandemic, nurses have realised the importance of documenting nursing care. INTRODUCTION: It is important to recognise how real nursing data match the ICNP reference set in SNOMED CT as that is the terminology to be used in Iceland. METHODS: A descriptive study with two methods: (a) statistical analysis of demographic and coded clinical data identified and retrieved from Electronic Health Record (EHR) and (b) mapping of documented nursing diagnoses and interventions in EHRs into ICNP 2019, ICNP 2021 and SNOMED CT 2021. RESULTS: The sample consisted of all (n = 91) adult COVID-19 patients admitted to the National University Hospital between 28 February and 30 June 2020. Nurses used 62 different diagnoses and 79 interventions to document nursing care. Diagnoses and interventions were best represented by SNOMED CT (85.4%; 100%), then by ICNP 2019 version (79.2%; 85%) and least by the ICNP 2021 reference set (70.8; 83.3%). Ten nursing diagnoses did not have a match in the ICNP 2021 reference set. DISCUSSION: Nurses need to keep up with the development of ICNP and submit to ICN new terms and concepts deemed necessary for nursing practice for inclusion in ICNP and SNOMED CT. CONCLUSION: Not all concepts in ICNP 2019 for COVID-19 patients were found to have equivalence in ICNP 2021. SNOMED CT-preferred terms cover the description of COVID-19 patients better than the ICNP 2021 reference set in SNOMED CT. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Through the use of ICNP, nurses can articulate the unique contribution made by the profession and make visible the specific role of nursing worldwide.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis.

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

In-situ fabrication of 3D interior hotspots templated with a protein@Au core-shell structure for label-free and on-site SERS detection of viral diseases

Nanoscale plasmonic hotspots play a critical role in the enhancement of molecular Raman signals, enabling the sensitive and reliable trace analysis of biomedical molecules via surface-enhanced Raman spectroscopy (SERS). However, effective and label-free SERS diagnoses in practical fields remain challenging because of clinical samples' random adsorption and size mismatch with the nanoscale hotspots. Herein, we suggest a novel SERS strategy for interior hotspots templated with protein@Au core-shell nanostructures prepared via electrochemical one-pot Au deposition. The cytochrome c and lysates of SARS-CoV-2 (SLs) embedded in the interior hotspots were successfully functionalized to confine the electric fields and generate their optical fingerprint signals, respectively. Highly linear quantitative sensitivity was observed with the limit-of-detection value of 10-1 PFU/ mL. The feasibility of detecting the targets in a bodily fluidic environment was also confirmed using the proposed templates with SLs in human saliva and nasopharyngeal swabs. These interior hotspots templated with the target analytes are highly desirable for early and on-site SERS diagnoses of infectious diseases without any labeling processes.

Implementing clinical guidelines for co-occurring substance use and major mental disorders in Swedish forensic psychiatry: An exploratory, qualitative interview study with mental health care staff.

INTRODUCTION: Patients with substance use disorders (SUD) and co-occurring mental disorders (COD) within forensic psychiatric care often suffer poor treatment outcomes and high rates of criminal recidivism, substance use, and psychiatric problems. This study aimed to describe the conditions for, and mental health care staff's experiences with, implementing integrated SUD-focused clinical guidelines, including assessment and treatment for patients with COD at a high-security forensic mental health services (FMHS) facility in Sweden. METHODS: Study staff conducted nineteen semi-structured interviews with health care staff experienced in administering the new SUD assessment and treatment. The study conducted a thematic analysis to describe the health care staff's experiences with these guidelines and suggestions for improvement. RESULTS: Most participants reported appreciation for the implementation of clinical guidelines with an SUD focus, an area they considered to have previously been neglected, but also noted the need for more practical guidance in the administration of the assessments. Participants reported the dual roles of caregiver and warden as difficult to reconcile and a similar, hindering division was also present in the health care staff's attitudes toward SUD. Participants' reports also described an imbalance prior to the implementation, whereby SUD was rarely assessed but treatment was still initiated. One year after the implementation, an imbalance still existed, but in reverse: SUD was more frequently assessed, but treatment was difficult to initiate. CONCLUSIONS: Despite indications of some ambivalence among staff regarding the necessity of the assessment and treatment guidelines, many participants considered it helpful to have a structured way to assess and treat SUD in this patient group. The imbalance between frequent assessment and infrequent treatment may have been due to difficulties transitioning patients across the "gap" between assessment and treatment. To bridge this gap, mental health services should make efforts to increase patients' insight concerning their SUD, flexibility in the administration of treatment, and the motivational skills of the health care staff working with this patient group. Participants considered important for enhancing treatment quality a shared knowledge base regarding SUD, and increased collaboration between different professions and between in- and outpatient services.

High Prevalence of Alternative Diagnoses in Children and Adolescents with Suspected Long COVID-A Single Center Cohort Study.

BACKGROUND: Long COVID (LC) is a diagnosis that requires exclusion of alternative somatic and mental diseases. The aim of this study was to examine the prevalence of differential diagnoses in suspected pediatric LC patients and assess whether adult LC symptom clusters are applicable to pediatric patients. MATERIALS AND METHODS: Pediatric presentations at the Pediatric Infectious Diseases Department of the University Hospital Essen (Germany) were assessed retrospectively. The correlation of initial symptoms and final diagnoses (LC versus other diseases or unclarified) was assessed. The sensitivity, specificity, negative and positive predictive values of adult LC symptom clusters were calculated. RESULTS: Of 110 patients, 32 (29%) suffered from LC, 52 (47%) were diagnosed with alternative somatic/mental diseases, and 26 (23%) remained unclarified. Combined neurological and respiratory clusters displayed a sensitivity of 0.97 (95% CI 0.91-1.00) and a negative predictive value of 0.97 (0.92-1.00) for LC. DISCUSSION/CONCLUSIONS: The prevalence of alternative somatic and mental diseases in pediatric patients with suspected LC is high. The range of underlying diseases is wide, including chronic and potentially life-threatening conditions. Neurological and respiratory symptom clusters may help to identify patients that are unlikely to be suffering from LC.

[Changes in number of emergency medical service deployments in an urban area during the first COVID-19 pandemic-related contact restriction phase]. / Veränderungen der rettungsdienstlichen Einsatzzahlen in einer Metropolregion während der ersten COVID-19-Pandemie-bedingten Kontaktbeschränkungsphase.

BACKGROUND: To contain the coronavirus disease (COVID-19) pandemic, public life was reduced through contact restriction measures (referred to as "lockdown" in the further course for reading simplicity), among other things to make health system resources available for the treatment of COVID-19 patients. In parallel, a decrease in emergency patients was observed in the public health system. METHODS: For two 10-week periods before and during the lockdown, ambulance service deployment rates were analysed in 6 ambulance service areas for 6 tracer diagnoses. Random effects were minimised by comparing the results with the corresponding 2018 and 2019 time periods and a calculated expected value. RESULTS: For emergency ambulance service calls, there was a reduction in call numbers (-16%) during the lockdown. A 20% reduction for the categories cardiac and cerebral ischaemia was found. In the urban area, the reduction in cardiac ischaemia was less pronounced at 14% than in the surrounding area at 23%. The deployment figures for intoxications decreased by 27% and for psychiatric emergencies by 16%. CONCLUSION: The public ambulance service was not overwhelmed by the COVID-19 pandemic; there was a decrease in depolyments during the lockdown. For the reduction in cerebral or cardiac ischaemias, the explanatory models for the influence of the lockdown are missing. Further studies on the utilisation behaviour of the ambulance service during a lockdown appear necessary in order to detect potentially fatal reductions in utilisation for the patient outcome and to be able to counteract them through education.

Correlates of long-COVID-19: the role of demographics, chronic illness, and psychiatric diagnosis in an urban sample.

Long-COVID-19 symptoms are an emerging public health issue. This study sought to investigate demographics, chronic illness, and probable psychiatric diagnoses as correlates for long-COVID-19 in an urban adult sample. Self-report Qualtrics surveys were sent to students across City University of New York (CUNY) campuses in New York City in Winter 2021-2022. Binary logistic regressions were used to assess the relation of a range of factors with endorsement of long-COVID-19. Results demonstrated that Latinx participants endorsed higher odds of long-COVID-19, as compared to non-Latinx white participants. Participants who endorsed having a prior chronic illness and those who met the cut-off for probable psychiatric diagnoses all endorsed higher odds of long-COVID-19. Long-COVID-19 may be more likely among specific subpopulations and among persons with other ongoing physical and mental illness.

Excess deaths from COVID-19 among Medicare beneficiaries with psychiatric diagnoses: community versus nursing home.

BACKGROUND: Psychiatric illness may pose an additional risk of death for older adults during the COVID-19 pandemic. Older adults in the community versus institutions might be influenced by the pandemic differently. This study examines excess deaths during the COVID-19 pandemic among Medicare beneficiaries with and without psychiatric diagnoses (depression, anxiety, bipolar disorder, and schizophrenia) in the community versus nursing homes. METHODS: This is a retrospective cohort study of a 20% random sample of 15,229,713 fee-for-service Medicare beneficiaries, from January 2019 through December 2021. Unadjusted monthly mortality risks, COVID-19 infection rates, and case-fatality rates after COVID-19 diagnosis were calculated. Excess deaths in 2020, compared to 2019 were estimated from multivariable logistic regressions. RESULTS: Of all included Medicare beneficiaries in 2020 (N = 5,140,619), 28.9% had a psychiatric diagnosis; 1.7% lived in nursing homes. In 2020, there were 246,422 observed deaths, compared to 215,264 expected, representing a 14.5% increase over expected. Patients with psychiatric diagnoses had more excess deaths than those without psychiatric diagnoses (1,107 vs. 403 excess deaths per 100,000 beneficiaries, p < 0.01). The largest increases in mortality risks were observed among patients with schizophrenia (32.4% increase) and bipolar disorder (25.4% increase). The pandemic-associated increase in deaths with psychiatric diagnoses was only found in the community, not in nursing homes. The increased mortality for patients with psychiatric diagnoses was limited to those with medical comorbidities. The increase in mortality for psychiatric diagnoses was associated with higher COVID-19 infection rates (1-year infection rate = 7.9% vs. 4.2% in 2020), rather than excess case fatality. CONCLUSIONS: Excess deaths during the COVID-19 pandemic were disproportionally greater in beneficiaries with psychiatric diagnoses, at least in part due to higher infection rates. Policy interventions should focus on preventing COVID-19 infections and deaths among community-dwelling patients with major psychiatric disorders in addition to those living the nursing homes.

Fusion Based Self Diagnosis Expert System Empowered with Leven-berg Marquardt Algorithm

COVID-19 coronavirus disease is the latest virus in the new century. The World Health Organization- WHO organization announced that COVID-19 disease is a pandemic that leads to thousands of death in short time of spam. A quick and accurate diagnosis of COVID-19 shows an important role in its prevention. This study is based on a fusion-based Self-Diagnosis Expert System Empowered by the Leven-berg Marquardt Algorithm for the diagnosis of diseases. Leven-berg Marquardt has been implemented for the classification of different symptoms of the diseases and relates the results for their diagnosis. The MatLab software was used for the simulation purpose. The proposed fusion-based LB increased the accuracy in the training and validation process to be 10 times more efficient than the existing. The fusion technique achieved an overall accuracy of 98.86%, and 99.09% in all performance metrics which included TNR, precision, and FPR statistical parameters. © 2022 IEEE.

Nurses' Work-Related Mental Health in 2017 and 2020-A Comparative Follow-Up Study before and during the COVID-19 Pandemic.

The COVID-19 pandemic put a lot of strain on healthcare organizations. Nurses account for over 50% of healthcare staff, and how nurses perform in their work is influenced by a number of human and work environmental factors. However, to our knowledge, there has not been a previous study with the intention to look at all areas that affect a sustainable working life and how these impact nurses' mental well-being. The aim of this study is to investigate the association between, and the effect of, different factors in nurses' work situations associated with nurses' work-related mental-health diagnoses, before and during the COVID-19 pandemic. A questionnaire was sent out to all 9219 nurses in the Swedish county of Skane in the spring of 2017 and during wave two of the COVID-19 pandemic in the fall of 2020. The data were analyzed through logistic regression analysis. The results showed that lack of joy in the daily work, an increased workload and lack of support from co-workers had an increased association with work-related mental-health diagnoses. Future research regarding the long-term impact of COVID-19 on all areas of nurses' professional and personal lives is needed.

In-situ fabrication of 3D interior hotspots templated with a protein@Au core–shell structure for label-free and on-site SERS detection of viral diseases

Nanoscale plasmonic hotspots play a critical role in the enhancement of molecular Raman signals, enabling the sensitive and reliable trace analysis of biomedical molecules via surface-enhanced Raman spectroscopy (SERS). However, effective and label-free SERS diagnoses in practical fields remain challenging because of clinical samples' random adsorption and size mismatch with the nanoscale hotspots. Herein, we suggest a novel SERS strategy for interior hotspots templated with protein@Au core–shell nanostructures prepared via electrochemical one-pot Au deposition. The cytochrome c and lysates of SARS-CoV-2 (SLs) embedded in the interior hotspots were successfully functionalized to confine the electric fields and generate their optical fingerprint signals, respectively. Highly linear quantitative sensitivity was observed with the limit-of-detection value of 10−1 PFU/mL. The feasibility of detecting the targets in a bodily fluidic environment was also confirmed using the proposed templates with SLs in human saliva and nasopharyngeal swabs. These interior hotspots templated with the target analytes are highly desirable for early and on-site SERS diagnoses of infectious diseases without any labeling processes.

Application of CRISPR/Cas Systems in the Nucleic Acid Detection of Infectious Diseases.

The CRISPR/Cas system is a protective adaptive immune system against attacks from foreign mobile genetic elements. Since the discovery of the excellent target-specific sequence recognition ability of the CRISPR/Cas system, the CRISPR/Cas system has shown excellent performance in the development of pathogen nucleic-acid-detection technology. In combination with various biosensing technologies, researchers have made many rapid, convenient, and feasible innovations in pathogen nucleic-acid-detection technology. With an in-depth understanding and development of the CRISPR/Cas system, it is no longer limited to CRISPR/Cas9, CRISPR/Cas12, and other systems that had been widely used in the past; other CRISPR/Cas families are designed for nucleic acid detection. We summarized the application of CRISPR/Cas-related technology in infectious-disease detection and its development in SARS-CoV-2 detection.

Multi-Class Flat Classification of Lung Diseases Utilizing Deep Learning

Early diagnosis of diseases is very critical for recovery. However, this is not always feasible due to the limited available staff or expensive and inadequate tools as we have witnessed in the recent COVID-19 pandemic. Lung diseases are life-threatening, but fortunately, they can be detected from X-ray images, which are cost-effective approaches. However, they need experts who are sometimes unavailable. Thus, using cutting-edge technology to diagnose diseases automatically and fast is the key solution to saving people's lives. In this research, deep learning techniques have been utilized to classify several lung diseases in a cost-saving, time-saving, and efficient manner. Examples of lung diseases studied in this research are COVID-19, Lung Opacity, Pneumonia, and Tuberculosis. Several pre-trained deep learning models have been employed for flat multi-class classification of these lung diseases instead of using binary classification to recognize one disease from normal cases, as most state-of-the-art studies carry out. The models' performance has been evaluated on imbalanced data of X-ray images with various resolutions and types. Finally, multiple measurements metrics have been utilized to evaluate the performance. The best accuracy achieved in this research is 95.643%. © 2022 IEEE.