A Randomized Controlled Trial of Active Stretching of the Hamstrings and Core Control for Low Back Pain and Musculoskeletal Discomfort during Prolonged Sitting among Young People.

Introduction: Stretching exercises are important in both the prevention and treatment of nonspecific low back pain (NLBP). The aim of this trial was to determine whether an 8-week active hamstring stretching protocol combined with core stabilization and education regarding the maintenance of a neutral lumbar spine during activities could reduce NLBP and low back discomfort during prolonged sitting among young people. Methods: Participants (52 students aged 18-25) were randomly assigned to one of two groups: the Experimental group (the hamstring stretching group) and the Control group (only education). The intervention was conducted for 8 weeks. The primary outcome measures were pain intensity (VAS), low back discomfort (LBD), and functional disability (ODI). The secondary outcome measures were satisfaction with the intervention (GPE) and flexibility of the hamstring (SLR). Results: After the 8-week intervention in the E-group, results of VAS, ODI, and LBD were significantly lower comparing to baseline. In the C-group, no significant differences were observed. After the exercises program, there were differences between the E-group and C-group in VAS, LBD, GPE, and SLR tests (p < 0.05, p < 0.05, p < 0.05, and p < 0.0001, respectively). Conclusions: In conclusion, our study provides compelling evidence that an eight-week program of active hamstring stretching and core stabilization exercises can significantly reduce NLBP and musculoskeletal discomfort during prolonged sitting in young adults. These findings highlight the importance of targeted exercise interventions in managing and preventing NLBP, particularly among sedentary populations. Further research is warranted to confirm these findings and explore their applicability to broader populations and over extended periods.

Prevalence of spine pain among Tunisian children and adolescents and related factors.

BACKGROUND: The prevalence of back and neck pain is common in children and adolescents, and in some series the numbers are alarming. Various risk factors have been identified, although some are controversial. OBJECTIVE: To determine the prevalence of neck and back pain in children and adolescents and to investigate the potential association with various risk factors identified in the literature. METHODS: We established a questionnaire targeting parents of children and adolescents aged between 6 and 18 years old in Tunisia. The recruitment of participants was done online using the Google Forms application. The questionnaire was divided into 2 parts: Part one collected the sociodemographics characteristics of the participants : age, gender, body mass index (BMI), exposure to passive smoking, the practice of a physical activity, puberty status and age at puberty if applicable, type and weight of the schoolbag, mean daily time spent on electronic devices, type of school the child attends (private/public), mode of transport from home to school, parental history of neck and/or back pain (mid or low back pain (LBP)), posture of the sitting position of the child, and finally whether the child reports neck/ back pain. The second part was aimed at parents whose child reported neck and/or back pain. We asked about the weekly frequency of neck/back pain, school absenteeism due to neck/back pain, whether it prevented the child from practicing physical activity and, finally, whether the child had ever seen a doctor/chiropractor/physiotherapist for their neck/back pain. RESULTS: Eighty-eight children (45 females, 43 males) were enrolled. Mean age was 11.9 ± 3.8 years [6-18]. Mean BMI was 18.8 ± 4.2 [15.8-35.5]. Thirty-four (38.6%) were pubescent. Twenty-five (28.4%) children were exposed to passive smoking. Parental history of spine pain was found in 58% of cases. A poor sitting position was noted in n = 49 (55.7%). Mean daily screen time was 88.3 ± 75.56 min [0-360]. Prevalence of spine pain was 44% (n = 39) distributed as follows: neck pain (n = 21, 23.8%), mid back pain (n = 15, 17%), LBP (n = 26, 29.5%), neck, mid back and low back pain (n = 4, 4.5%) Professional help seeking for spine pain in children was reported by 15 participants (25.3%). Among them, 20.3% visited a physician and 5% consulted a chiropractor or physiotherapist. A significant correlation was found between spine pain and age (p = 0.006) and BMI (p = 0.006). A significant association was found between LBP and exposure to passive smoking, puberty status, type of school bag and poor posture. A positive parental history of spine pain was significantly associated with the presence of spine pain in their children with p = 0.053 (neck pain), p = 0.013 (back pain) and p < 0.00 (LBP) respectively. A significant association was found between the presence of spine pain and school absenteeism, participation in sports, consultation with a doctor or physiotherapist/chiropractor (p < 0.0001 respectively). CONCLUSION: The prevalence of spinal pain was frequent in our series. A positive parental history of spinal pain, a bad posture while sitting, passive smoking, use of backpack, higher age and higher BMI were potential associated factors.

Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm.

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset. • We use the concepts of LBP and GLCM to extract features from the skin lesion images. • The Gray Wolf Optimization (GWO) algorithm is employed for feature selection. • A decoder based on Transformers is utilized for melanoma classification.

Prevalence of Low Back Pain and Its Associated Factors Among Medical Students at Nineveh University in Iraq: A Cross-Sectional Study.

Background Low back pain (LBP) is a common problem encountered in medical practice, leading to limitations in daily activities and causing social and economic hardships. Objectives This study aimed to assess the prevalence of LBP and its associated factors among medical students at Nineveh University in Iraq. Methods Between December 2022 and January 2023, a cross-sectional study was conducted among medical students at Nineveh University. A modified version of the Standard Nordic Questionnaire was used for data collection. Results Out of 308 students, 229 (74.4%) experienced LBP at some point in their lives. In addition, 209 (67.9%) reported having LBP during the last 12 months, 148 (48.1%) during the previous seven days, and 126 (40.9%) at the time of answering the survey. Factors significantly associated with LBP during the last 12 months were being in the fifth-stage academic year (p=0.047), family history of LBP (p=0.003), and history of trauma (p=0.006). On the multivariable logistic regression analysis, factors significantly associated with LBP during the last 12 months were family history of LBP (p=0.02) and history of trauma (p=0.01). Conclusions The prevalence of LBP among medical students at Nineveh University was comparatively high. A family history of LBP and a history of trauma were factors significantly associated with LBP during the last 12 months. Managing this health concern should be a priority for the administration of medical schools.

LPS-LBP complex induced endothelial cell pyroptosis in aortic dissection is associated with gut dysbiosis.

Acute aortic dissection (AAD) is the most severe traumatic disease affecting the aorta. Pyroptosis-mediated vascular wall inflammation is a crucial trigger for AAD, and the exact mechanism requires further investigation. In this study, our proteomic analysis showed that Lipopolysaccharide (LPS)-binding protein (LBP) was significantly upregulated in the plasma and aortic tissue of patients with AAD. Further, 16S rRNA sequencing of stool samples suggested that patients with AAD exhibit gut dysbiosis, which may lead to an impaired intestinal barrier and LPS leakage. By comparing with control mice, we found that LBP, including Pyrin Domain Containing Protein3 (NLRP3), the CARD-containing adapter apoptosis-associated speck-like protein (ASC), and Cleaved caspase-1, were upregulated in the AAD aorta, whereas gut intestinal barrier-related proteins were downregulated. Moreover, treated with LBPK95A (an LBP inhibitor) attenuated the incidence of AAD, the expression levels of pyroptosis-related factors, and the extent of vascular pathological changes compared to those in AAD mice. In addition, LPS and LBP treatment of human umbilical vein endothelial cells (HUVECs) activated TLR4 signaling and intracellular reactive oxygen species (ROS) production, which stimulated NLRP3 inflammasome formation and mediated pyroptosis in endothelial cells. Our findings showed that gut dysbiosis mediates pyroptosis by the LPS-LBP complex, thus providing new insights into developing AAD.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Role of End Plate Changes and Paraspinal Muscle Pathology in Lower Back Pain: A Narrative Review.

Degenerative changes of the lumbar intervertebral disc are the most significant causes of enduring lower back pain. The possibility of the diagnosis is limited in people with this low back pain. Therefore, it is essential to identify the relevant back pain subgroups. The paraspinal muscles, that is, the muscles that attach to the spine, are necessary for the proper functioning of the spine and the body; insufficiency can result in back pain. Lower back pain disorders are strongly associated with altered function or structure of these paraspinal muscles, especially fibrosis and fatty infiltration. Modic changes are the bone marrow changes of the end plate in the vertebral body seen on MRI. These are strongly related to degeneration of the disc and are common in individuals with back pain symptoms. Articles were selected from Google Scholar using the terms 'Modic changes,' 'end plate changes,' 'paraspinal muscles,' and 'lower back pain. ' This article compiled different studies aiming to enhance the comprehension of biochemical processes resulting in the development of lumbar pain. Search using the keywords 'Modic changes,'' end plate changes lower back pain,' 'paraspinal muscles lower back pain,' and 'Modic changes lower back pain' on Google Scholar yielded 33000, 41000, 49400, and 17,800 results, and 958, 118, 890 and 560 results on Pubmed respectively.

Research on image recognition of tomato leaf diseases based on improved AlexNet model.

Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.

Effects of Multimodal Physical Therapy on Pain, Disability, H-reflex, and Diffusion Tensor Imaging Parameters in Patients With Lumbosacral Radiculopathy Due to Lumbar Disc Herniation: A Preliminary Trial.

Background Lumbosacral radiculopathy (LSR) due to lumbar disc herniation (LDH) is a condition caused by mechanical compression of nerve roots. Various physical therapy interventions have been proposed for the conservative management of LSR due to LDH. However, the study of physical therapy interventions in a multimodal form is lacking. Additionally, the effect of physical therapy on diffusion tensor imaging (DTI) parameters of the compressed nerve root has not been studied. This study aimed to investigate the effects of multimodal physical therapy (MPT) on pain, disability, soleus H-reflex, and DTI parameters of the compressed nerve root in patients with chronic unilateral LSR due to LDH. Methods A prospective preliminary pre-post clinical trial with a convenience sample was conducted. A total of 14 patients with chronic unilateral LSR due to paracentral L4-L5 or L5-S1 LDH were recruited for the study. Participants received a total of 18 sessions of a six-week MPT program that consisted of electrophysical agents, manual therapy interventions, and core stability exercises. Electrophysical agents involved interferential current and hot pack. Manual therapy interventions included myofascial release, side posture positional distraction, passive spinal rotation mobilization, and high-velocity low-amplitude manipulation. Visual analog scale (VAS), Roland-Morris Disability Questionnaire (RMDQ), soleus H-reflex amplitude, side-to-side amplitude (H/H) ratio, fractional anisotropy (FA), and apparent diffusion coefficient (ADC) of the compressed nerve root were measured at baseline and post-intervention. Results There were significant improvements in VAS, RMDQ, H/H ratio, FA, and ADC of the compressed nerve root. Furthermore, significant improvement was found in the affected side compared with the contralateral side in H-reflex amplitude. Conclusions The observations of this preliminary trial suggest that MPT is a successful intervention in patients with chronic unilateral LSR due to LDH. Regarding DTI parameters of the compressed nerve root, FA increased and ADC decreased. Future studies with a control group, large sample sizes, and longer follow-up periods are needed.

The Association of Intestinal Leak with Sarcopenia and Physical Disability in Patients with Various Stages of Chronic Kidney Disease.

Sarcopenia is related to disease severity in chronic kidney disease (CKD) patients; however, its pathophysiology remains poorly known. We investigated the associations of biomarkers of intestinal leak with sarcopenia in various stages of CKD. We recruited 61-76-year-old male controls and patients with various stages of CKD (n = 36-57/group) for measuring plasma lipopolysaccharide-binding protein (LBP) and zonulin (markers of intestinal leak), handgrip strength (HGS), skeletal mass index (SMI), and gait speed (markers of sarcopenia), and short physical performance battery (SPPB; marker of physical capacity). CKD stages 4 and 5 were associated with lower HGS, SMI, gait speed, and cumulative SPPB scores and a higher sarcopenia prevalence than controls and patients with CKD stages 1 and 2 (all p < 0.05). CKD patients (stages 1 and 2) had elevated plasma zonulin and LBP when compared with CKD stages 4 and 5. Plasma zonulin and LBP exhibited significant correlations with renal function, HGS, gait speed, SPPB scores, and oxidative stress markers in CKD stages 4 and 5 (all p < 0.05). However, similar relations were not found in early CKD. Collectively, intestinal leak may be contributing to sarcopenia and physical disability in the advanced stages of CKD.

Evolving Spinal Treatment Modalities: A Review of the Literature on Non-surgical Interferential Differential Dynamics (IDD) Therapy.

Low back pain is one of the most common ailments encountered by physicians and orthopedic surgeons. There are various modalities used to treat low back pain, including conservative management, and a few of them involve rest, medications, massage, bracing, acupuncture, and physical therapy. Though most of the patients improve with conservative management, the burden of this disease has been very high and caused a significant amount of economic loss. Therefore, in-depth knowledge of all conservative methods is essential for physicians managing low back pain. Furthermore, there can be many causes of low back pain. Some of the more common ones are mechanical back pain due to paraspinal muscles or facetal in origin, discogenic back pain, and sacroiliac joint dysfunction. Many patients, especially the older population, have the discogenic origin as the more common cause of back pain, and traction therapy has been used for its treatment for ages. In this review, we discuss non-surgical spinal decompression/traction therapy popularly known as interferential differential dynamics (IDD) therapy with its current standing and recent advancement.

Past, present, and future of microbiome-based therapies.

Technological advances in studying the human microbiome in depth have enabled the identification of microbial signatures associated with health and disease. This confirms the crucial role of microbiota in maintaining homeostasis and the host health status. Nowadays, there are several ways to modulate the microbiota composition to effectively improve host health; therefore, the development of therapeutic treatments based on the gut microbiota is experiencing rapid growth. In this review, we summarize the influence of the gut microbiota on the development of infectious disease and cancer, which are two of the main targets of microbiome-based therapies currently being developed. We analyze the two-way interaction between the gut microbiota and traditional drugs in order to emphasize the influence of gut microbial composition on drug effectivity and treatment response. We explore the different strategies currently available for modulating this ecosystem to our benefit, ranging from 1st generation intervention strategies to more complex 2nd generation microbiome-based therapies and their regulatory framework. Lastly, we finish with a quick overview of what we believe is the future of these strategies, that is 3rd generation microbiome-based therapies developed with the use of artificial intelligence (AI) algorithms.

Effect of Quadratus Lumborum Block in Patients With Acute-Subacute Unilateral Lumbar Strain.

INTRODUCTION: Lumbar strain originating from the quadratus lumborum (QL) is an important cause of low back pain; however, its diagnosis is often missed, and treatment is often inadequate. This leads to unnecessary diagnostic investigations and chronicization of pain. Therefore, it is important to treat it effectively and safely. In this study, we aimed to find out the effect of ultrasound (US)-guided QL block in acute-subacute low back pain caused by a strain of QL. MATERIALS AND METHODS: Our study was retrospective, and the changes in the visual analog scale (VAS) and Oswestry Disability Index (ODI) scores within one week in 50 patients with acute-subacute localized low back pain, unilateral lumbar strain, palpation tenderness, paravertebral spasm, and decreased lumbar range of motion in the QL muscle, in whom we applied US-guided block to the QL muscle, were analyzed. RESULTS: There was a significant decrease in the mean VAS and ODI scores of the patients after the procedure. There was a negative correlation between improvement rates in VAS and ODI scores and age and body mass index (BMI). Recovery rates were higher in female patients than in male patients. CONCLUSION: It can be said that US-guided QL block is an effective treatment method for QL-induced lumbar strains, and younger age, female gender, and lower BMI are associated with better responses after injection.

Evaluation of the Relationship Between Smartphone Addiction and Lower Back Pain and Neck Pain Among Medical Students at Al-Baha University.

Introduction In recent years, the increased use of smartphones has adversely affected students, leading to issues like musculoskeletal pain. Therefore, our objective was to assess the correlation between smartphone addiction and neck and lower back pain. Methodology An observational cross-sectional study was conducted at Al-Baha University, Al-Baha, Saudi Arabia. The Smartphone Addiction Scale Short Version (SAS-SV) was used to measure the level of smartphone addiction while the Nordic Musculoskeletal Questionnaire (NMQ) was utilized to evaluate musculoskeletal pain. Results Smartphone addiction was prevalent in 72% of the participants (n = 293). Significantly, lower back pain was associated with smartphone addiction (p-value = 0.004). However, none of the demographic characteristics were associated with neck or lower back pain (p-value > 0.05). Students in clinical years had a higher risk of neck pain than those in an internship (p-value = 0.048). Conclusion Almost two-thirds of the students were addicted to smartphones, with a significant association with lower back pain. Students addicted to their smartphones had a higher risk of developing lower back pain, while clinical-year students had a higher risk of developing neck pain. It's important to raise awareness about the health and safety dangers linked to smartphones and other devices.

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.

Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.

Low back pain prevention behaviors and beliefs among the Polish population in a cross-sectional survey.

Background: Low back pain (LBP) is one of the most common problems of public health and creates a burden globally. The aim was to assess the Polish population's back pain prevention behaviors and beliefs and to examine how these health behaviors and beliefs vary across sociodemographic factors and physical activity. Methods: A cross-sectional survey was carried out among 208 randomly selected patients of the public general practitioner clinic. The differences in LBP-related beliefs and attitudes were determined due to participants' status of requiring or non-requiring LBP treatment. Results: More than half of the respondents did not engage in behaviors that protect against back pain. Individuals with higher education levels and those who exercised at least once a week were significantly more likely to adopt behaviors to protect their backs. Less than half of the participants reported having a workplace that was adequately prepared to protect against back pain, and only 35.1% of the participants reported receiving instruction while taking up work on how to avoid back pain while working. According to respondents' opinions, preventive actions are necessary to protect against back pain. Inappropriate exercises and stress can be contributors to back pain, with these opinions reported more often by women and participants with higher education levels. Participants who received treatment for LBP showed a significantly higher expression of behaviors to protect against back pain compared to participants who did not require treatment. However, there were no significant differences in participants' beliefs about back pain prevention between the group requiring LBP treatment and the group not requiring LBP treatment. Conclusion: The study provides valuable insights into the association between LBP treatment, back pain prevention behaviors, and beliefs, suggesting potential avenues for future research and intervention development. By addressing workplace ergonomics and promoting a culture of back health, it may be possible to reduce the burden of LBP in Poland.

The high-throughput solid-phase extraction of cis-cyclo(L-Leu-L-Pro) and cis-cyclo(L-Phe-L-Pro) from Lactobacillus plantarum demonstrates efficacy against multidrug-resistant bacteria and influenza A (H3N2) virus.

Introduction: 2,5-diketopiperazines are the simplest forms of cyclic dipeptides (CDPs) and have diverse frameworks with chiral side chains that are useful for drug development. Previous research has investigated the antimicrobial properties of proline-linked CDPs and their combinations in the culture filtrate (CF) of Lactobacillus plantarum LBP-K10 using anion exchange chromatography (AEC). However, the quantity of CDPs showcasing notable anti-influenza virus activity derived from AECs was generally lower than those originating from Lactobacillus CF. Methods: To address this issue, the study aims to propose a more efficient method for isolating CDPs and to introduce the antiviral combinations of CDPs obtained using a new method. The study employed a novel technique entailing high-throughput C18-based solid-phase extraction with a methanol gradient (MeSPE). The MeSPE method involved increasing the methanol concentration from 5% to 50% in 5% increments. Results: The methanol SPE fractions (MeSPEfs) eluted with methanol concentrations between 35% and 45% evinced substantial efficacy in inhibiting the influenza A/H3N2 virus via plaque-forming assay. MeSPEf-45, the 45% MeSPEf, exhibited exceptional efficacy in preventing viral infections in Madin-Darby kidney cells, surpassing both individual CDPs and the entire set of MeSPEfs. To identify the specific antiviral components of MeSPEf-45, all MeSPEfs were further fractionated through preparative high-performance liquid chromatography (prep-HPLC). MeSPEf-45 fractions S8 and S11 presented the highest activity against multidrug-resistant bacteria and influenza A/H3N2 virus among all MeSPEfs, with 11 common fractions. Antiviral fractions S8 and S11 were identified as proline-based CDPs, specifically cis-cyclo(L-Leu-L-Pro) and cis-cyclo(L-Phe-L-Pro), using gas chromatography-mass spectrometry. The combination of MeSPEf-45 fractions S8 and S11 displayed superior antibacterial and anti-influenza virus effects compared to the individual fractions S8 and S11. Discussion: High-throughput MeSPE-derived MeSPEfs and subsequent HPLC-fractionated fractions presents an innovative approach to selectively purify large amounts of potent antimicrobial CDPs from bacterial CF. The findings also show the effectiveness of physiologically bioactive combinations that utilize fractions not containing CDP. This study provides the initial evidence demonstrating the antimicrobial properties of CDPs acquired through high-throughput SPE techniques.

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images.

Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods.

Human and Porcine Lumbar Endplate Injury Risk in Repeated Flexion-Compression.

Low back pain (LBP) affects 50-80% of adults at some point in their lifetime, yet the etiology of injury is not well understood. Those exposed to repeated flexion-compression are at a higher risk for LBP, such as helicopter pilots and motor vehicle operators. Animal injury models offer insight into in vivo injury mechanisms, but interspecies scaling is needed to relate animal results to human. Human (n = 16) and porcine (n = 20) lumbar functional spinal units (FSUs) were loaded in repeated flexion-compression (1 Hz) to determine endplate fracture risk over long loading exposures. Flexion oscillated from 0 to 6° and peak applied compressive stress ranged from 0.65 to 2.38 MPa for human and 0.64 to 4.68 MPa for porcine specimens. Five human and twelve porcine injuries were observed. The confidence intervals for human and porcine 50% injury risk curves in terms of stress and cycles overlapped, indicating similar failure behavior for this loading configuration. However, porcine specimens were more tolerant to the applied loading compared to human, demonstrated by a longer time-to-failure for the same applied stress. Optimization revealed that time-to-failure in human specimens was approximately 25% that of porcine specimens at a given applied stress within 0.65-2.38 MPa. This study determined human and porcine lumbar endplate fracture risks in long-duration repeated flexion-compression that can be directly used for future equipment and vehicle design, injury prediction models, and safety standards. The interspecies scale factor produced in this study can be used for previous and future porcine lumbar injury studies to scale results to relevant human injury.

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.

Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.