Search

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT–BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis. [ABSTRACT FROM AUTHOR]

Inflammatory periodontal and peri-implant diseases follow dysbiotic shifts in a susceptible host. A well-established tool for microbial sample collection is the use of paper points. The purpose of this pilot study was to evaluate the use of interdental brushes compared to paper points. Biofilm samples were collected with paper points and later interdental brushes from ten patients. Five patients were represented with a community periodontal index of treatment needs (CPITN) of 0–2 around the teeth and an implant with PPD ≤ 5 mm and no radiological bone loss. The remaining five patients had a CPITN ≥ 3 and one implant with peri-implantitis. Microbial samples were analyzed with quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS). The results showed higher amounts of DNA in samples taken by interdental brushes but also higher Ct values. Both methods detected Filifactor alocis, Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia, and Treponema denticola in the majority of samples, while Aggregatibacter actinomycetemcomitans was rarely found. A microbial dysbiosis index showed comparable or higher values in sites with no periodontitis/peri-implantitis with interdental brushes. The results of this pilot study indicate that interdental brushes might be a valid technique for microbial sampling and particularly advantageous in the early detection of dysbiotic shifts around teeth and implants. Larger studies with more participants are needed to validate the proposed microbial sampling method with interdental brushes. [ABSTRACT FROM AUTHOR]

The MNS7 (Mia) blood group antigen is found at a different prevalence among different ethnic groups. Anti-Mia can cause hemolytic disease of the fetus and newborn (HDFN) and both acute- and delayed-type hemolytic transfusion reactions (HTR). Mia typing should be performed in donors to prevent life-threatening hemolytic transfusion reactions. The gel card and standard tube methods still need specialized equipment, centrifugation, and expertise for result interpretation. We used a novel paper-based analytical device (PAD) pre-coated with monoclonal IgM anti-Mia for Mia phenotyping. We measured grey pixel intensity in blood typing results for interpretation processing using OpenCV at the sample (SP) and elution parts (EP); furthermore, we used the SP: EP ratio and F-score as analysis criteria. We typed 214 blood EDTA samples with PAD–Mia and then compared with gel card results for setting an analysis criterion. We observed 100% sensitivity, specificity, and accuracy when we applied the SP: EP ratio and F-score with the optimal criterion (1.07 and 0.17 for SP: EP ratio and F-score, respectively). The validation of PAD–Mia typing for blood donor samples (n = 150) via F-score gave 100% sensitivity and specificity when compared with the gel card method; therefore, we argue that PAD–Mia typing can be used for Mia phenotyping without sero-centrifugation. Moreover, to study the correlation between genotype and phenotype, PCR-SSP was performed to identify GYP(B-A-B) hybrids. The results revealed that all Mia+ blood samples gave a positive with GP. Hut, GP. HF, GP. Mur, GP. Hop, and GP. Bun. Results of the gel card method and PCR-SSP were concordant. Hence, using PAD–Mia typing in blood donors would be helpful for creating a phenotype database of blood donors for reducing alloimmunization risks. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis remains a serious public health issue, and Brazil was among the seven countries with the highest prevalence of this disease worldwide. The measures to control this disease are not easily developed, and the improvement of its diagnosis, surveillance, and control is still needed. This study aimed to carry out the polymerase chain reaction (PCR) diagnosis of Leishmania infantum in vector samples in some municipalities of the State of São Paulo, which included two municipalities with human disease transmission and two with dog transmission only. Vectors were collected in traps with luminous bait. Next, they were killed at −4 °C and kept in 70% alcohol. Groups of ten female insects (pools) were mashed on cation exchange paper (fine cellulose phosphate with 18 µEq/cm² ionic exchange capacity) for DNA extraction. The PCR was carried out to identify the natural infection of the Leishmania genus in female Lutzomyia longipalpis (Lu. Longipalpis). Out of the 3,880 Lu. longipalpis phlebotomines, 1060 were female and 2820 were male (3:1). The method used to extract the DNA in pools of ten phlebotomines and the PCR resulted in sensitivity, specificity, practicality, and faster analyses when compared to the individual analysis method. The procedure described can be used on a large scale in the leishmaniasis epidemiological surveillance, enabling a higher number of analyses and the optimization of human resources because the traditional diagnostic method is carried out via desiccation of the insect digestive system and microscopic examination, which is time-demanding and there is the need of manual skills. [ABSTRACT FROM AUTHOR]

Subclinical Myocardial Dysfunction in Patients with Persistent Dyspnea One Year after COVID-19 - Why Should Screening for Cardiovascular Diseases Be Performed? The presence of persistent dyspnea was observed in one-third of the recovered COVID-19 patients, in line with the current data on the long-term evolution of COVID-19 [[3]]. A total of 66 COVID-19 patients without a previous history of cardiorespiratory diseases were evaluated at 12 months for potential subclinical cardiac dysfunction following the acute infection [[2]]. [Extracted from the article]

None of the currently available parameters allow for a direct and objective measurement of vaginal moisture. We used a calibrated filter paper strip as a measurement tool for the quantification of vaginal fluid, in a similar manner as the ophthalmic "Schirmer test" (used for eye moisture measurement). The study aimed to evaluate the validity of this new, objective tool, to measure vaginal moisture. We compared vaginal moisture measurements using the "modified Schirmer test" in symptomatic women with genitourinary syndrome of menopause to those of women without vaginal dryness. The mean "modified Schirmer test" measurement in the control group was 21.7 mm compared to 3.3 mm in the study group, yielding a statistically significant difference (p < 0.001). Strong correlations were found between "modified Schirmer test" measurements and pH (correlation coefficient −0.714), Vaginal Health Index [VHI (0.775)], and Visual Analogue Score (VAS) of dryness during intercourse (−0.821). Our findings suggest that the "modified Schirmer test" can be used as an objective measurement for the assessment of vaginal fluid level. This test may also prove useful for evaluation of non-hormonal treatments aimed to treat vaginal dryness. [ABSTRACT FROM AUTHOR]

Background: The lack of a clear answer regarding the efficacy of physiotherapy in the treatment of cubital tunnel syndrome (CuTS) has led to attempts to critically assess the scientific studies conducted to date. Materials and Methods: Two databases (MEDLINE via PubMed and PEDro) and Google Scholar were used to search for papers. The inclusion criteria were randomized controlled trials, case series, and case reports that evaluate the effects of physiotherapy in the treatment of patients with CuTS. Results: A total of 18 studies met the eligibility criteria, capturing a total of 425 participants. Seven papers were randomized controlled trials, three more described prospective studies without a control group, and eight papers contained case reports. An analysis of the literature evaluating the effectiveness of various forms of broadly defined physiotherapy indicates that their use can have a beneficial effect in reducing many subjective and objective symptoms and improving function. In the majority of papers included in this review, their authors indicated positive therapeutic effects. Only one randomized controlled trial reported no change following therapy. It can therefore be stated that the results of the research conducted so far are optimistic. However, only 7 of the 18 papers were randomized controlled trials, while 3 were prospective studies, and 8 papers were case studies, in which 23 people with CuTS were studied. Conclusions: The small number of randomized clinical trials and their considerable heterogeneity do not allow firm conclusions to be drawn about the effectiveness of physiotherapy in the conservative treatment of CuTS. [ABSTRACT FROM AUTHOR]

Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are important liver enzymes in clinical settings. Their levels are known to be elevated in individuals with underlying liver diseases and those consuming hepatotoxic drugs. Serum ALT and AST levels are crucial for diagnosing and assessing liver diseases. Serum ALT is considered the most reliable and specific candidate as a disease biomarker for liver diseases. ALT and AST levels are routinely analyzed in high-risk individuals for the bioanalysis of both liver function and complications associated with drug-induced liver injury. Typically, ALT and AST require blood sampling, serum separation, and testing. Traditional methods require expensive or sophisticated equipment and trained specialists, which is often time-consuming. Therefore, developing countries have limited or no access to these methods. To address the above issues, we hypothesize that low-cost biosensing methods (paper-based assays) can be applied to the analysis of ALT and AST levels in biological fluids. The paper-based biodetection technique can semi-quantitatively measure ALT and AST from capillary finger sticks, and it will pave the way for the development of an inexpensive and rapid alternative method for the early detection and diagnosis of liver diseases. This method is expected to significantly reduce the economic burden and aid routine clinical analysis in both developed and underdeveloped countries. The development of low-cost testing platforms and their diagnostic utility will be extremely beneficial in helping millions of patients with liver disorders. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome (SARS-CoV-2), the causative agent of the global pandemic, which has resulted in more than one million deaths with tens of millions reported cases, requires a fast, accurate, and portable testing mechanism operable in the field environment. Electrochemical sensors, based on paper substrates with portable electrochemical devices, can prove an excellent alternative in mitigating the economic and public health effects of the disease. Herein, we present an impedance biosensor for the detection of the SARS-CoV-2 spike protein utilizing the IgG anti-SARS-CoV-2 spike antibody. This label-free platform utilizing screen-printed electrodes works on the principle of redox reaction impedance of a probe and can detect antigen spikes directly in nasopharyngeal fluid as well as virus samples collected in the universal transport medium (UTM). High conductivity graphene/carbon ink is used for this purpose so as to have a small background impedance that leads to a wider dynamic range of detection. Antibody immobilization onto the electrode surface was conducted through a chemical entity or a biological entity to see their effect; where a biological immobilization can enhance the antibody loading and thereby the sensitivity. In both cases, we were able to have a very low limit of quantification (i.e., 0.25 fg/mL), however, the linear range was 3 orders of magnitude wider for the biological entity-based immobilization. The specificity of the sensor was also tested against high concentrations of H1N1 flu antigens with no appreciable response. The most optimized sensors are used to identify negative and positive COVID-19 samples with great accuracy and precision. [ABSTRACT FROM AUTHOR]

However, the GCW and GWI values of the two groups, both with and without dyspnea, do not deviate from the normal reference values, which are based on the NORRE study [[2]] and that were underpinned by the authors. The study reports the following echocardiographic differences: Compared to asymptomatic patients, patients with dyspnea presented lower LV constructive work (GCW): 2183.7 ± 487.9 vs. 2483.1 ± 422.4, I p i = 0.024; and a lower global work index (GWI): 1960.0 ± 396.2 vs. 2221.1 ± 407.9, I p i = 0.030. This study compares 23 patients with self-reported dyspnea and 43 without 1 year after hospital admission for COVID-19. [Extracted from the article]

Paraquat is a highly toxic herbicide. Paraquat poisoning is often fatal and is an important public health threat in many places. The quick identification and timely initiation of treatment based on timely analysis of the paraquat concentration in urine/serum could improve the prognosis for patients. However, current paraquat concentration measurements are time-consuming and difficult to implement due to the expensive and bulky equipment required. To address these practical challenges, paper-based devices have emerged as alternative diagnostic tools for improving point-of-care testing. In this study, we demonstrate the successful use of a paper-based analytical device for the accurate detection of urine paraquat concentration. The developed paper-based analytical device employs colorimetric paraquat concentration measurements. The R2 value for the urine paraquat standard curve was 0.9989, with a dynamic range of 0–100 ppm. The limit of detection was 3.01 ppm. Two other optical-based approaches, Spectrochip and NanoDrop, were used for comparison. The results suggest that the developed paper-based analytical device is comparable to other colorimetric measurements, as determined by Bland–Altman analysis. The device was clinically validated using urine from six paraquat-poisoned patients. The results prove that the developed paper-based analytical device is accurate, easy-to-use, and efficient for urine paraquat concentration measurement, and may enable physicians to improve clinical management. [ABSTRACT FROM AUTHOR]

Background Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. Methods: A total of 394 samples were collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing–Bablok regression and Bland–Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. Results: The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Passing–Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. The resulting positive and negative predicted values were 0.77 and 0.92, respectively, vs. 0.46 and 0.96 from the external formula. Conclusions: Total cholesterol analysis in dried blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection. [ABSTRACT FROM AUTHOR]

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are the leading cause of death, globally. Stroke and heart attacks account for more than 80% of cardiovascular disease-related deaths. To prevent patient mismanagement and potentially save lives, effective screening at an early stage is needed. Diagnosis is typically made using an electrocardiogram (ECG) analysis. However, ECG recordings are often corrupted by different types of noise, degrading the quality of the recording and making diagnosis more difficult. This paper reviews research on automatic ECG quality assessment techniques used in studies published from 2012–2022. The CinC11 Dataset is most often used for training and testing algorithms. Only one study tested its algorithm on people in real-time, but it did not specify the demographic data of the subjects. Most of the reviewed papers evaluated the quality of the ECG recordings per single lead. The accuracy of the algorithms reviewed in this paper range from 85.75% to 97.15%. More clarity on the research methods used is needed to improve the quality of automatic ECG quality assessment techniques and implement them in a clinical setting. This paper discusses the possible shortcomings in current research and provides recommendations on how to advance the field of automatic ECG quality assessment. [ABSTRACT FROM AUTHOR]

Despite the potential in fabrication of microfluidic paper-based analytical devices (μPADs) for point-of-care testing (POCT) kits, the development of simple, accurate, and rapid devices with higher sensitivity remains challenging. Here, we report a novel method for 3D-μPAD fabrication with enclosed channels using vat photopolymerization to avoid fluid evaporation. In detail, height of the enclosed channels was adjusted from 0.3 to 0.17 mm by varying the UV exposure time from 1 to 4 s for the top barrier, whereas the exposure time for the bottom and side barriers was fixed. As a result, sample flow in the enclosed channels of 3D-μPADs showed lesser wicking speed with very scant evaporation compared to that in the hemi channels in the 3D-μPADs. The stoppage of evaporation in the enclosed channels significantly improved the gray intensity and uniformity in the detection zone of the 3D-μPADs, resulting in as low as 0.3 mM glucose detection. Thus 3D-μPADs with enclosed channels showed enhanced sensitivity compared to the 3D-μPADs with hemi channels when dealing with a small volume sample. Our work provides a new insight into 3D-μPAD design with enclosed channels, which redefines the methodology in 3D printing. [ABSTRACT FROM AUTHOR]

Detecting small amounts of analyte in clinical practice is challenging because of deficiencies in specimen sample availability and unsuitable sampling environments that prevent reliable sampling. Paper-based analytical devices (PADs) have successfully been used to detect ultralow amounts of analyte, and origami-based PADs (O-PADs) offer advantages that may boost the overall potential of PADs in general. In this study, we investigated two potential clinical applications for O-PADs. The first O-PAD we investigated was an origami-based enzyme-linked immunosorbent assay (ELISA) system designed to detect different concentrations of rabbit IgG. This device was designed with four wing structures, each of which acted as a reagent loading zone for pre-loading ELISA reagents, and a central test sample loading zone. Because this device has a low limit of detection (LOD), it may be suitable for detecting IgG levels in tears from patients with a suspected viral infection (such as herpes simplex virus (HSV)). The second O-PAD we investigated was designed to detect paraquat levels to determine potential poisoning. To use this device, we sequentially folded each of two separate reagent zones, one preloaded with NaOH and one preloaded with ascorbic acid (AA), over the central test zone, and added 8 µL of sample that then flowed through each reagent zone and onto the central test zone. The device was then unfolded to read the results on the test zone. The three folded layers of paper provided a moist environment not achievable with conventional paper-based ELISA. Both O-PADs were convenient to use because reagents were preloaded, and results could be observed and analyzed with image analysis software. O-PADs expand the testing capacity of simpler PADs while leveraging their characteristic advantages of convenience, cost, and ease of use, particularly for point-of-care diagnosis. [ABSTRACT FROM AUTHOR]

Paper-based colorimetric biosensors combine the use of paper with colorimetric signal detection. However, they usually demonstrate lower sensitivities because a signal amplification procedure has not been used. Stopping the reaction of colorimetric signal generation is often used in lab-based assays in order to amplify and stabilize the colorimetric signal for detection. In this study, the generation of a stopped colorimetric signal was examined for accurate and enhanced signal detection in paper-based biosensors. The colorimetric reaction in biosensors is usually based on the interaction between the enzyme horseradish peroxidase (HRP) and a selected chromogenic substrate. The two most commonly used HRP substrates, 3,3',5,5'-tetramethylbenzidine (TMB) and 2'-azinobis (3-ethylbenzothiazoline-6-sulfonic-acid) (ABTS), were compared in terms of their ability to generate a stopped colorimetric signal on membrane. The stopped colorimetric signal was visible for TMB but not for ABTS. Moreover, the generation of stopped colorimetric signal was dependent on the presence of polyvinylidene-difluoride (PVDF) membrane as the separation layer. With PVDF the colorimetric signal (color intensity) was higher (TMB: 126 ± 6 and ABTS: 121 ± 9) in comparison to without PVDF (TMB: 110 ± 2 and ABTS: 102 ± 4). The TMB stopped colorimetric signal demonstrated a more stable signal detection with lower standard deviation values. To conclude, a stopped colorimetric signal can be generated in paper-based biosensors for enhanced and accurate signal detection. [ABSTRACT FROM AUTHOR]

Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery. [ABSTRACT FROM AUTHOR]

Salivary biomarkers are indicators of many biological and pathological conditions and provide further information regarding the early detection of diseases. This bibliometric analysis aims to identify and evaluate the scientific literature addressing salivary biomarkers from a dental perspective, to identify the most prolific organizations, authors, journals, countries, and keywords used within this research domain. An electronic search was performed using Elsevier's Scopus database. From a total of 587 retrieved papers (published between 1997 and 2021), 399 were selected. For the data analysis and its visualization, the title of the articles, year of publication, countries, authors, journals, articles, and keywords were analyzed using Microsoft Excel and VOSviewer (a bibliometric software program). An increase in the number of publications was identified from 1997 to 2021. The United States (U.S.) published the most papers (84) and received the highest citations (3778), followed by India and Brazil. The Journal of Periodontology published the highest number of articles (39) that received the highest citations. The University of Kentucky from the U.S. published most of the papers related to salivary biomarkers that received the highest citations. Timo Sorsa published the most papers (14 papers), while Craig Miller was the highest cited author (754 citations). Concerning the highly cited papers, a paper by Micheal et al., published in 2010, received the highest citations (487 citations). "Saliva", followed by "human", were the most common keywords used by the authors in the papers related to salivary biomarkers. The findings of this analysis revealed an increase in salivary biomarker-related publications that positively influenced the number of citations each paper received. The U.S. produced the most publications that received the highest citations, and the University of Kentucky, U.S., was the most prominent. The articles were mostly published in the Journal of Periodontology and received the highest number of citations. [ABSTRACT FROM AUTHOR]

The diagnosis of Alzheimer's disease (AD) is frequently missed or delayed in clinical practice. To remedy this situation, we developed a screening, paper-based (P-ELISA) platform to detect β-amyloid peptide 1–42 (Aβ42) and provide rapid results using a small volume, easily accessible plasma sample instead of cerebrospinal fluid. The protocol outlined herein only requires 3 μL of sample per well and a short operating time (i.e., only 90 min). The detection limit of Aβ42 is 63.04 pg/mL in a buffer system. This P-ELISA-based approach can be used for early, preclinical stage AD screening, including screening for amnestic mild cognitive impairment (MCI) due to AD. It may also be used for treatment and stage monitoring purposes. The implementation of this approach may provide tremendous impact for an afflicted population and may well prompt additional and expanded efforts in both academic and commercial communities. [ABSTRACT FROM AUTHOR]

Diagnostic biomarkers are key components of diagnostics. In this paper, we consider diagnostic biomarkers taking continuous values that are associated with a dichotomous disease status, called malignant or benign. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. We assume that, together with the disease status, one control and multiple experimental biomarkers are collected from each subject to test if any of the experimental biomarkers have a larger AUC than the control. In this case, each experimental biomarker will be compared with the control so that a multiple testing issue is involved in the comparisons. In this paper, we propose a simple non-parametric statistical testing procedure to compare K (≥ 2) experimental biomarkers with a control, adjusting for the multiplicity and its sample size calculation method. Our sample size formula requires the specification of the AUC values (or the standardized effect size of each biomarker between the benign and malignant groups) together with the correlation coefficients between the biomarkers, the prevalence of the malignant group in the study population, the type I error rate, and the power. Through simulations, we show that the statistical test controls the overall type I error rate accurately and the proposed sample size closely maintains the specified statistical power. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning in medical imaging has significantly enhanced the capabilities of artificial intelligence while simultaneously introducing challenges, including the need for vast amounts of training data and the labor-intensive tasks of labeling and segmentation. Generative adversarial networks (GANs) have emerged as a solution, offering synthetic image generation for data augmentation and streamlining medical image processing tasks through models such as cGAN, CycleGAN, and StyleGAN. These innovations not only improve the efficiency of image augmentation, reconstruction, and segmentation, but also pave the way for unsupervised anomaly detection, markedly reducing the reliance on labeled datasets. Our investigation into GANs in medical imaging addresses their varied architectures, the considerations for selecting appropriate GAN models, and the nuances of model training and performance evaluation. This paper aims to provide radiologists who are new to GAN technology with a thorough understanding, guiding them through the practical application and evaluation of GANs in brain imaging with two illustrative examples using CycleGAN and pixel2style2pixel (pSp)-combined StyleGAN. It offers a comprehensive exploration of the transformative potential of GANs in medical imaging research. Ultimately, this paper strives to equip radiologists with the knowledge to effectively utilize GANs, encouraging further research and application within the field. [ABSTRACT FROM AUTHOR]

Globally, type 2 diabetes mellitus (T2DM) is a major threat to the public's health, particularly in low- and middle-income countries (LMICs). The production of short-chain fatty acids (SCFAs) by the gut microbiota has been reported to have the potential to reduce the prevalence of T2DM, particularly in LMICs where the disease is becoming more common. Dietary fibers are the primary source of SCFAs; they can be categorized as soluble (such as pectin and inulin) or insoluble (such as resistant starches). Increased consumption of processed carbohydrates, in conjunction with insufficient consumption of dietary fiber, has been identified as a significant risk factor for type 2 diabetes (T2DM). However, there are still controversies over the therapeutic advantages of SCFAs on human glucose homeostasis, due to a lack of studies in this area. Hence, a few questions need to be addressed to gain a better understanding of the beneficial link between SCFAs and glucose metabolism. These include the following: What are the biochemistry and biosynthesis of SCFAs? What role do SCFAs play in the pathology of T2DM? What is the most cost-effective strategy that can be employed by LMICs with limited laboratory resources to enhance their understanding of the beneficial function of SCFAs in patients with T2DM? To address the aforementioned questions, this paper aims to review the existing literature on the protective roles that SCFAs have in patients with T2DM. This paper further discusses possible cost-effective and accurate strategies to quantify SCFAs, which may be recommended for implementation by LMICs as preventive measures to lower the risk of T2DM. [ABSTRACT FROM AUTHOR]

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research. [ABSTRACT FROM AUTHOR]

The cell-free genomic DNA (gDNA) concentration in serum ranges from 1500 to 7500 copies/mL within 2 h after phlebotomy (6–24 times the concentration observed in plasma). Here, we aimed to evaluate the gDNA size distribution in serum with time after coagulation and to test if crude serum can be directly used as a source of gDNA for qPCR. Next, we investigated if single nucleotide polymorphisms (SNPs) could be genotyped directly from the crude serum isolated from capillary blood using a hand-powered paper centrifuge. All tested PCR targets (65, 100, 202 and 688 base pairs) could be successfully amplified from DNA extracted from serum, irrespective of their amplicon size. The observed qPCR quantitation cycles suggested that the genomic DNA yield increased in serum with incubation at room temperature. Additionally, only 65 and 101 base pair qPCR targets could be amplified from crude serum soon after the coagulation. Incubation for 4 days at room temperature was necessary for the amplification of PCR targets of 202 base pairs. The 688 base pair qPCR target could not be amplified from serum directly. Lastly, serum was successfully separated from capillary blood using the proposed paper centrifuge and the genotypes were assigned by testing the crude serum using allele-specific qPCR, producing small amplicon sizes in complete agreement with the genotypes assigned by testing the DNA extracted from whole blood. The serum can be used directly as the template in qPCR for SNP genotyping, especially if small amplicon sizes are applied. This shortcut in the SNP genotyping process could further molecular point-of-care diagnostics due to elimination of the DNA extraction step. [ABSTRACT FROM AUTHOR]

Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These CNN algorithms, in addition to requiring huge amounts of data, can identify higher- and lower-level features that are significant while classifying tumors into benign or malignant. However, the existing literature is limited in terms of the explainability of the resultant classification, and identifying the exact features that are of importance, which is essential in the decision-making process for healthcare practitioners. Thus, the motivation of this work is to implement a custom classifier on the ovarian tumor dataset, which exhibits high classification performance and subsequently interpret the classification results qualitatively, using various Explainable AI methods, to identify which pixels or regions of interest are given highest importance by the model for classification. The dataset comprises CT scanned images of ovarian tumors taken from to the axial, saggital and coronal planes. State-of-the-art architectures, including a modified ResNet50 derived from the standard pre-trained ResNet50, are implemented in the paper. When compared to the existing state-of-the-art techniques, the proposed modified ResNet50 exhibited a classification accuracy of 97.5 % on the test dataset without increasing the the complexity of the architecture. The results then were carried for interpretation using several explainable AI techniques. The results show that the shape and localized nature of the tumors play important roles for qualitatively determining the ability of the tumor to metastasize and thereafter to be classified as benign or malignant. [ABSTRACT FROM AUTHOR]

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition's stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: ( C 1 ) reflects clinical and scientific consensus, ( C 2 ) likelihood of possible harm, ( C 3 ) evidence of correct reasoning, ( C 4 ) evidence of correct comprehension, ( C 5 ) evidence of correct retrieval, and ( C 6 ) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for C 1 , to 130 (97.74%) for C 2 , to 131 (98.50%) for C 3 , to 133 (100%) for C 4 , to 132 (99.25%) for C 5 , and to 122 (91.73%) for C 6 , while PaLM2 to 81 questions (60.90%) for C 1 , to 114 (85.71%) for C 2 , to 115 (86.47%) for C 3 , to 124 (93.23%) for C 4 , to 113 (84.97%) for C 5 , and to 93 (69.92%) for C 6 . Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare. [ABSTRACT FROM AUTHOR]

This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View–University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative. [ABSTRACT FROM AUTHOR]

Mesenteric ischemia diagnosis is challenging, with an overall mortality of up to 50% of cases despite advances in treatment. The main problem that affects the outcome is delayed diagnosis because of non-specific clinical presentation. Multi-Detector CT Angiography (MDCTA) is the first-line investigation for the suspected diagnosis of vascular abdominal pathologies and the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extent and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases, helping clinicians and surgeons with appropriate management. CT (Computed Tomography) can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM = non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA (CT Angiography) protocol; 2. to explain the CTA arterial and venous supply to the gastrointestinal tract and the relevant ischemic pattern; and 3. to describe vascular, bowel, and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia. [ABSTRACT FROM AUTHOR]

Neck infections are often prone to being underestimated and can manifest insidiously. The spread of infection can lead to translocation into thoracic areas, causing descending necrotizing mediastinitis (DNM). However, the application of the post-mortem approach in such cases is not well-described in the literature. A literature review was carried out according to the PRISMA methods. Nine papers were included in the final review, revealing different levels of involvement of neck layers that can be linked to different causes. Expertise with respect to the anatomy of the fasciae and spaces of the neck enables an understanding of the pathogenesis of DNM. However, a clear autoptic description was not provided in any of the articles. Therefore, we also employed a practical post-mortem approach to cases of death due to DNM. It is fundamental for pathologists to identify the exact head and neck structures involved. Providing dissectors with support from an otolaryngologist could be useful. This paper could help address such difficult cases. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. [ABSTRACT FROM AUTHOR]

An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one. [ABSTRACT FROM AUTHOR]

Foreign bodies (FBs) in the nasal cavity are commonly encountered in otorhinolaryngology. This retrospective study was conducted between January 2009 and December 2020. The nasal FB frequencies were investigated according to age and sex. The type, shape, and location of the FBs, onset, and clinical features were obtained. In a total of 1228 cases, the average age was 3.9 ± 5.7 years. We found a higher incidence in patients 2–4 years old. The most frequent nasal FBs were beads (24.0%), toys/plastics (17.8%), and beans/corn (15.8%). However, there were some differences in the nasal FB types according to the age group. There was no difference in the location of the nasal FBs according to age group, but nasal FBs were found more frequently in the right nasal cavity in the 1–3-years-old groups. Most patients (97.5%) visited the hospital the same day when the nasal FB insertion was suspected, and most were asymptomatic. After the removal of the nasal FBs, most patients were free of complications. Of the nine cases involving button batteries, septal perforation was observed in two patients who visited the hospital one or two days after the FB insertion. There were specific age-based characteristics of the nasal FBs that may serve as a basis for specific precautions. [ABSTRACT FROM AUTHOR]

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to provide a comprehensive overview of the role of fluoro-5α-dihydrotestosterone ([18F]-FDHT) for the in vivo imaging of androgen receptors (AR) through positron emission tomography (PET) in metastatic breast (mBC) and metastatic castration-resistant prostate cancer (mCRPC). Relevant studies published from 2013 up to May 2023 were selected by searching Scopus, PubMed and Web of Science. The selected imaging studies were analyzed using a modified version of the critical Appraisal Skills Programme (CASP). Eleven studies encompassing 321 patients were selected. Seven of the eleven selected papers included 266 subjects (82.2%) affected by mCRPC, while four encompassed 55 (17.2%) patients affected by mBC. [18F]-FDHT PET showed a satisfying test/retest reproducibility, and when compared to a histochemical analysis, it provided encouraging results for in vivo AR quantification both in mCRPC and mBC. [18F]-FDHT PET had a prognostic relevance in mCRPC patients submitted to AR-targeted therapy, while a clear association between [18F]-FDHT uptake and the bicalutamide response was not observed in women affected by AR-positive mBC. Further studies are needed to better define the role of [18F]-FDHT PET, alone or in combination with other tracers (i.e., [18F]-FDG/[18F]-FES), for patients' selection and monitoring during AR-targeted therapy, especially in the case of mBC. [ABSTRACT FROM AUTHOR]

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect's complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The '3D surface deviation' between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the '3D surface deviation' between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential. [ABSTRACT FROM AUTHOR]

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ , θ , α , and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. [ABSTRACT FROM AUTHOR]

Given the pronounced impact COVID-19 continues to have on society—infecting 700 million reported individuals and causing 6.96 million deaths—many deep learning works have recently focused on the virus's diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices. [ABSTRACT FROM AUTHOR]

Blood glucose stability in diabetic patients determines the degree of health, and changes in blood glucose levels are related to the outcome of diabetic patients. Therefore, accurate monitoring of blood glucose has a crucial role in controlling diabetes. Aiming at the problem of high volatility of blood glucose concentration in diabetic patients and the limitations of a single regression prediction model, this paper proposes a method for predicting blood glucose values based on particle swarm optimization and model fusion. First, the Kalman filtering algorithm is used to smooth and reduce the noise of the sensor current signal to reduce the effect of noise on the data. Then, the hyperparameter optimization of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) models is performed using particle swarm optimization algorithm. Finally, the XGBoost and LightGBM models are used as the base learner and the Bayesian regression model as the meta-learner, and the stacking model fusion method is used to achieve the prediction of blood glucose values. In order to prove the effectiveness and superiority of the method in this paper, we compared the prediction results of stacking fusion model with other 6 models. The experimental results show that the stacking fusion model proposed in this paper can accurately predict blood glucose values, and the average absolute percentage error of blood glucose prediction is 13.01%, and the prediction error of the stacking fusion model is much lower than that of the other six models. Therefore, the proposed diabetes blood glucose prediction method in this paper has superiority. [ABSTRACT FROM AUTHOR]

Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year. [ABSTRACT FROM AUTHOR]

Objectives: This systematic review aimed to analyse the published evidence for the use of non-invasive methods for the early detection of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). Methods: The literature was systematically searched through several databases: PubMed, Cochrane Library, and Web of Science. Additional exploration was performed through cross-checks on the bibliographies of selected reviews. The inclusion criteria involved studies assessing the application of non-invasive tests on humans in the screening, diagnosis, or surveillance of OSCC or OPMDs and reporting sensitivity (SE) and specificity (SP). The Newcastle–Ottawa scale (NOS) was applied to assess the quality of the studies included. Results: The search strategy resulted in 8012 preliminary records. After a duplicate check, 116 titles remained. After abstract analysis, 70 papers remained. After full text analysis, only 54 of the 70 papers fit the inclusion criteria (28 were original articles and 26 were reviews). Those 26 reviews were used to manually search for further original articles. From this last search, 33 original articles were found. Thus, a total of 61 original studies were included and investigated. Findings from this systematic review indicate useful information, such as a description of the mechanisms, ease of use, limitations, and SE and SP values, to drive the choice of the optimal minimally invasive method to be utilized as an adjunctive tool to examine the suspicious lesions. Conclusions: Each of the analysed tools can be improved or implemented, considering their high SE and low SP. Despite advancements, incisional biopsy continues to be the gold standard for the definitive diagnosis of oral cancer and precancerous lesions. Further research and development are essential to improving the sensitivity, specificity, and reliability of non-invasive tools for widespread clinical application. [ABSTRACT FROM AUTHOR]

Cancer surgery is aimed at complete tumor resection and accurate lymph node detection. However, numerous blood vessels are distributed within the tumor, and the colors of the tumor, blood vessels, and lymph nodes are similar, making observations with the naked eye difficult. Therefore, tumors, blood vessels, and lymph nodes can be monitored via color classification using an operating microscope to induce fluorescence emission. However, as the beam width of the LED required to induce fluorescence emission is narrow and the power loss of the beam is significant at a certain working distance, there are limitations to inducing fluorescence emission, and light reflection occurs in the observation image, obstructing the view of the observation area. Therefore, the removal of reflected light is essential to avoid missing the diagnosis of the lesion under observation. This paper proposes the use of a beam mirror and polarizing filter to increase the beam width and beam intensity. The refraction and reflection effects of the beam were utilized using the beam mirror, and the rotation angle of the polarizing filter was adjusted to remove light reflection. Consequently, the minimum beam power using the beam mirror was 10.9 mW, the beam width was doubled to 40.2°, and more than 98% of light reflection was removed at 90° and 270°. With light reflection effectively eliminated, clear observation of lesions is possible. This method is expected to be used effectively in surgical, procedural, and diagnostic departments. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate. [ABSTRACT FROM AUTHOR]

This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) activate T cells, causing immune-related adverse events (irAEs). Skin manifestations are common among irAEs, but ICI-associated bullous pemphigoid (BP) is rare. Inhibiting programmed death (PD)-1 signaling, in addition to causing epitope spreading, may disrupt B and T cell balance, causing excessive autoantibody production against the skin's basement membrane, leading to BP. A 70-year-old woman developed late-onset multi-organ irAEs, including diarrhea, thyroid dysfunction, and BP, while receiving pembrolizumab, a PD-1 inhibitor. This highlights the long-term risk of irAEs, which can occur 2–3 years after starting ICIs. In cases of multi-organ irAE, C-reactive protein levels and neutrophil/lymphocyte ratio are often low. These characteristics were observed in our case. Few papers address multiple organ involvement, highlighting the need to consider irAEs in a multi-organ context. While it is known that drug-induced skin reactions worsen as blood eosinophil counts increase, in our case, the eosinophil count remained normal, suggesting that ICI-associated BP might have been controlled without discontinuing the ICI and through tapering of low-dose oral prednisone treatment. Additionally, in this case, significant CD4-positive T cell infiltration was observed in the immunostaining examination of the blisters, indicating that severe CD4-positive T cell infiltration induced by the ICI might have led to multi-organ involvement, including severe diarrhea. Few reports focus on blood eosinophil counts in BP cases or discuss CD4 and CD8 immunostaining in BP cases. Therefore, future research should explore the relationship between blood eosinophil counts, immunostaining results, and the prognosis of irAEs, including BP, in treatment courses. [ABSTRACT FROM AUTHOR]

Salivary gland tumors are a rare and heterogeneous group of neoplasms of the head and neck region. The mixed category of these tumors include the following entities: pleomorphic adenoma (PA), carcinoma ex pleomorphic adenoma (CEPA), salivary carcinosarcoma (CS), and metastasizing PA (MPA). The most common benign tumor of the salivary glands is PA. Metastasis and malignant degeneration have been reported in cases of PA of a salivary gland origin. Judging by their behavior, MPA, CEPA, and CS can be considered aggressive tumors. Invasive CEPA has been identified in the parotid gland more frequently. MPA and CS cases reported in the current literature are rare. In this paper, we present, narratively, the clinico-morphological features of this group of mixed tumors. [ABSTRACT FROM AUTHOR]

The aim of this paper is to demonstrate the difference in usefulness of the coronary artery calcium score (CACS) and the full assessment of the severity of coronary artery disease in coronary computed tomography angiography (CCTA) studies. The difference between the population risk of coronary artery disease (CAD) assessed by the CACS and the severity of CAD was demonstrated in images from two CCTA studies. The first image is from a patient with a CACS of 0 and significant coronary artery stenosis. In the native phase of CCTA examination, no calcified changes were detected in the topography of the coronary arteries. In the middle section of the left descending artery (LAD), at the level of the second diagonal branch (Dg2), a large non-calcified atherosclerotic plaque was visible. Mid-LAD stenosis was estimated to be approximately 70%. The second image features a patient with a high CACS but no significant coronary artery stenosis. The calcium score of individual coronary arteries calculated using the Agatston method was as follows: left main (LM) 0, LAD 403, left circumflex (LCx) 207.7, right coronary artery (RCA) 12. CACS was 622.7, representing a significant population risk of significant CAD. In the proximal and middle sections of the LAD, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visible, causing stenosis of 25–50%. Similarly, in the proximal and middle sections of the LCx, numerous calcified and mixed atherosclerotic plaques with positive remodeling were visualized, causing stenoses of 25–50%. Calcified atherosclerotic plaques were found in the RCA, causing stenosis <25%. The entire CCTA image met CAD-RADS 2 (coronary artery disease reporting and data system) criteria. In summary, CACS may be applicable in population-based studies to assess the risk of significant CAD. In the evaluation of individual patients, a comprehensive assessment of CAD severity based on the angiographic phase of the CCTA examination should be used. [ABSTRACT FROM AUTHOR]