Salivary Distinctiveness and Modifications in Males with Diabetes and Behçet's Disease

Efficacy of a Mobile Health-Based Behavioral Treatment for Lifestyle Modification in Type 2 Diabetes Self-Management: Greenhabit Randomized Controlled Trial

BACKGROUND

Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.

OBJECTIVE

This study evaluates whether the Greenhabit (mobile health [mHealth]) behavioral treatment enhances T2D outcomes compared with standard care.

METHODS

A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.25 years, SD 9.46 years) recently diagnosed with T2D. Participants were recruited face-to-face from primary care centers in Barcelona, Spain, between July 2021 and March 2022. They were randomly assigned to 1 of 2 groups: (1) an intervention group (n=61) instructed to use the Greenhabit mobile app alongside standard care, or (2) a control group (n=62) who received advice on maintaining a healthy diet and followed standard care. The Greenhabit app incorporates serious gaming technology. Participants received daily messages and challenges focused on promoting a healthy lifestyle, including nutrition, exercise, relaxation, a positive mindset, and a supportive social environment. The app encouraged participants to set weekly goals and awarded points for completing challenges. Data on nutrition, anthropometrics, and blood and urine samples were collected at baseline, 6 weeks, and 12 weeks. Questionnaires assessing quality of life, work-life balance, and social environment were administered at baseline and during the final visit. The primary outcomes were HbA1c and fasting plasma glucose (FPG). Repeated-measures analysis of variance was used to compare changes over time (baseline to 6 weeks and baseline to 12 weeks) between the 2 intervention groups. Analysis of covariance was performed to evaluate changes at 6 and 12 weeks, adjusted for baseline levels of each variable. Multiple contrasts were corrected using a Bonferroni post hoc test.

RESULTS

Both groups showed significant reductions in HbA1c after 6 and 12 weeks (mean change in the intervention group [n=50] -0.4%, P<.001 vs -0.3% in the control group [n=53], P=.001) and in FPG after 6 weeks (mean change in the intervention group -5.3 mg/dL, P=.01 vs control group -5.8 mg/dL, P=.01). At 12 weeks, the intervention group also showed significant reductions in systolic and diastolic blood pressures (mean change -4.5, P=.049 and -2.4 mmHg, P=.03, respectively), body weight (mean change -0.8 kg, P=.03), BMI (mean change -0.3 kg/m2, P=.03), waist circumference (mean change -1.0 cm, P=.046), and triglyceride concentration (mean change -20.0 mg/dL, P=.03). There was also a significant increase in high-density lipoprotein-cholesterol concentrations (mean change 2 mg/dL, P=.049). Finally, improvements were noted in 3 out of the 5 elements of balance: positivity, social environment, and work-life balance.

CONCLUSIONS

The 12-week intervention with the Greenhabit behavioral treatment mHealth app showed beneficial effects on T2D outcomes and reduced the burden of cardiovascular risk factors. Although larger studies are warranted, these results suggest that mHealth apps can be a promising tool for improving T2D self-management.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN13456652; http://www.isrctn.com/ISRCTN13456652.

Exosomes derived from bone marrow mesenchymal stem cells ameliorate chemotherapeutically induced damage in rats' parotid salivary gland

OBJECTIVE

A nanometer-sized vesicles originating from bone marrow mesenchymal stem cells (BMMSCs), called exosomes, have been extensively recognized. This study defines the impact of BMMSCs and their derived exosomes on proliferation, apoptosis and oxidative stress (OS) levels of CP-induced parotid salivary gland damage.

METHODS

BMMSCs were isolated from the tibia of four white albino rats and further characterized by flowcytometric analysis. BMMSCs-derived exosomes were harvested and underwent characterization using transmission electron microscopy (TEM), western blot analysis and BCA assay. Fifty-six healthy white albino male rats weighting from 200 to 250 g were allocated into 4 groups (n = 14); Group I, rats received phosphate buffered saline (PBS), group II, rats were intraperitoneally injected with CP, group III& IV received CP and after 3 days they were intravenously injected with either BMMSCs (group III) or BMMSCs-exosomes (group IV). Histological, and immunohistochemical studies using proliferating cell nuclear antigen (PCNA) were done after 7 and 14 days. The OS was measured using malondialdehyde (MDA) and apoptosis was measured by annexin V-FITC/PI.

RESULTS

BMMSCs and exosomes treated groups showed better histological features approximating the normal architecture of the control group. The percentage of PCNA positively stained cells were significantly higher in the exosomes treated group in comparison to all other groups. MDA assay test revealed that the exosomes were able to reduce the OS when compared to the cell-based therapy using BMMSCs. Annexin V revealed that BMMSCs-exosomes significantly reduced the percentage of apoptotic cells compared to other treated groups.

CONCLUSIONS

BMMSCs-exosomes could improve the CP-induced cytotoxicity in rats' parotid salivary gland.

Evaluation of salivary melatonin and MMP-9 levels in periodontal diseases

OBJECTIVE

The aim of this study was to evaluate salivary matrix metalloproteinase-9 (MMP-9) and melatonin levels in individuals with periodontal health, gingivitis, and periodontitis.

DESIGN

A total of 170 participants were enrolled in this study. They included 57 periodontally healthy individuals, 58 gingivitis patients, and 55 periodontitis patients. Saliva samples were collected by passive drool technique. The levels of MMP-9 and melatonin in saliva were measured biochemically using the ELISA method.

RESULTS

Salivary MMP-9 levels in the periodontitis group were significantly higher than those in the gingivitis and periodontally healthy groups, while salivary melatonin levels were significantly lower (p<0.001). A positive correlation was observed between clinical periodontal parameters and salivary MMP-9 levels, while salivary melatonin levels were negatively correlated (p<0.001). A negative correlation was also observed between salivary MMP-9 levels and salivary melatonin levels (p<0.001).

CONCLUSION

This study shows that the level of melatonin in saliva is associated with periodontal disease and with the level of MMP-9 in saliva, which plays a role in this disease.

Correlation analysis of AVPR1a and AVPR2 with abnormal water and sodium and potassium metabolism in rats

In clinical practice, an increasing number of patients exhibit concurrent cardiac and renal dysfunction, known as "cardiorenal syndrome," where each condition exacerbates the other, resulting in poorer patient prognosis. Fluid and sodium retention can lead to excessive fluid overload in the body; therefore, correcting fluid and sodium metabolic disorders is crucial for alleviating patient symptoms. This study was to investigate the abnormalities in water and sodium metabolism, as well as the expression levels of arginine vasopressin receptor 1a (AVPR1a) and arginine vasopressin receptor 2 (AVPR2), in a rat model of chronic renal failure-chronic heart failure (CRF-CHF). One hundred male Sprague-Dawley rats were randomly assigned into four groups: the CG group (normal feeding), the CRF group (3/4 nephrectomy using a "two-step surgical method"), the CHF group (subcutaneous injection of isoproterenol at 100 mg/kg), and the CRF-CHF group (3/4 nephrectomy followed by a subcutaneous injection of isoproterenol at 100 mg/kg 1 week later). 4 weeks post-surgery, urine and blood samples were collected to measure 24 h urinary protein, sodium, and potassium levels. Serum creatinine (SCr) and blood urea nitrogen (BUN) levels were determined using assay kits. Left ventricular end diastolic pressure (LVEDP) and left ventricular systolic pressure (LVSP) were measured via left ventricular catheterization. The heart was weighed to calculate the left ventricular weight to body weight ratio (LVW/BW). The renal cortex and medulla were isolated to assess the relative mRNA and protein expression levels of AVPR1a and AVPR2. Compared to the CG group, the CRF and CRF-CHF groups exhibited significantly elevated levels of 24 h urinary protein, SCr, BUN, and relative expression levels of AVPR1a and AVPR2 in the renal cortex and medulla. The CHF and CRF-CHF groups showed significant increases in LVEDP and LVW/BW (P < 0.05). Additionally, compared to the CG group, the other three groups had significantly increased urinary sodium and blood potassium levels, and significantly decreased urinary potassium and blood sodium levels (P < 0.05). Compared to the CRF and CHF groups, the CRF-CHF group exhibited significantly higher levels of 24 h urinary protein, SCr, BUN, and relative expression levels of AVPR1a and AVPR2 in the renal cortex and medulla, along with significantly increased LVEDP and LVW/BW, significantly reduced LVSP, significantly increased urinary sodium and blood potassium levels, and significantly decreased urinary potassium and blood sodium levels (P < 0.05). Rats with CRF-CHF experienced exacerbated renal and cardiac failure, characterized by significant disturbances in water and sodium metabolism and abnormal expression of AVPR1a and AVPR2.

A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study

BACKGROUND

Medical staff often conduct assessments, such as food intake and nutrient sufficiency ratios, to accurately evaluate patients' food consumption. However, visual estimations to measure food intake are difficult to perform with numerous patients. Hence, the clinical environment requires a simple and accurate method to measure dietary intake.

OBJECTIVE

This study aims to develop a food intake estimation system through an artificial intelligence (AI) model to estimate leftover food. The accuracy of the AI's estimation was compared with that of visual estimation for liquid foods served to hospitalized patients.

METHODS

The estimations were evaluated by a dietitian who looked at the food photo (image visual estimation) and visual measurement evaluation was carried out by a nurse who looked directly at the food (direct visual estimation) based on actual measurements. In total, 300 dishes of liquid food (100 dishes of thin rice gruel, 100 of vegetable soup, 31 of fermented milk, and 18, 12, 13, and 26 of peach, grape, orange, and mixed juices, respectively) were used. The root-mean-square error (RMSE) and coefficient of determination (R2) were used as metrics to determine the accuracy of the evaluation process. Corresponding t tests and Spearman rank correlation coefficients were used to verify the accuracy of the measurements by each estimation method with the weighing method.

RESULTS

The RMSE obtained by the AI estimation approach was 8.12 for energy. This tended to be smaller and larger than that obtained by the image visual estimation approach (8.49) and direct visual estimation approach (4.34), respectively. In addition, the R2 value for the AI estimation tended to be larger and smaller than the image and direct visual estimations, respectively. There was no difference between the AI estimation (mean 71.7, SD 23.9 kcal, P=.82) and actual values with the weighing method. However, the mean nutrient intake from the image visual estimation (mean 75.5, SD 23.2 kcal, P<.001) and direct visual estimation (mean 73.1, SD 26.4 kcal, P=.007) were significantly different from the actual values. Spearman rank correlation coefficients were high for energy (ρ=0.89-0.97), protein (ρ=0.94-0.97), fat (ρ=0.91-0.94), and carbohydrate (ρ=0.89-0.97).

CONCLUSIONS

The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.

Ultrasound-assisted magnetic dispersive micro-solid-phase extraction based on carbon quantum dots/zeolite imidazolate framework-90/polyvinyl pyrrolidone/Fe3O4 followed by high-performance liquid chromatography with ultraviolet detection for trace analysis of paracetamol and etodolac in human plasma

A four-part and sustainable nanocomposite composed of carbon quantum dots modified with zeolite imidazolate framework-90, polyvinyl pyrrolidone and magnetite (CQDs/ZIF-90/PVP/Fe3O4) was fabricated and applied in ultrasound-assisted magnetic dispersive micro-solid-phase extraction (US-A-MDMSPE). US-A-MDMSPE was followed by high-performance liquid chromatography with ultraviolet detection (HPLC-UV) for extraction, enrichment and simultaneous low-level monitoring of paracetamol (PCM) and etodolac (EDL) in human plasma. To increase the extraction yield and improve the sensitivity, nanohybrid arrays of metal-organic frameworks and conductive polymers were immobilized on the surface of CQDs followed by magnetization. The nano-extractor was characterized via Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM) and energy-dispersive X-ray spectroscopy (EDX). The recent method provided limits of detection (LODs) of 0.16 and 0.09 ng mL-1 for PCM and EDL, respectively. The calibration curve for PCM and EDL was linear in the range of 0.7-2000 ng mL-1 and 0.5-1200 ng mL-1 with a regression (r 2) value between 0.993 and 0.998, respectively. Acceptable precisions including intra-assay (≤6.9%) and inter-assay (≤8.3%) accuracies and notable accuracy (≤8.5%) were achieved to demonstrate the applicability of this method for the evaluation of the pharmacokinetic data of target drugs in human plasma.

Machine Learning-Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study

BACKGROUND

Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus (T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple risk factors.

OBJECTIVE

This study aimed to assess the validity and use of an ML model for predicting the 3-year incidence of ND in patients with T2DM.

METHODS

We used data from 2 independent cohorts-the discovery cohort (1 hospital; n=22,311) and the validation cohort (2 hospitals; n=2915)-to predict ND. The outcome of interest was the presence or absence of ND at 3 years. We selected different ML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort.

RESULTS

The study dataset included 22,311 (discovery) and 2915 (validation) patients with T2DM recruited between 2008 and 2022. ND was observed in 133 (0.6%) and 15 patients (0.5%) in the discovery and validation cohorts, respectively. The AdaBoost model had a mean AUROC of 0.82 (95% CI 0.79-0.85) in the discovery dataset. When this result was applied to the validation dataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%, sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost model were age and cardiovascular disease.

CONCLUSIONS

This study shows the use and feasibility of ML for assessing the incidence of ND in patients with T2DM and suggests its potential for use in screening patients. Further international studies are required to validate these findings.

Objective Analysis of Traditional Chinese Medicine Syndrome Differentiation of Patients With Diabetes and Prediabetes: Protocol for a Nonrandomized, Exploratory, Observational Case-Control Study Using Digitalized Traditional Chinese Medicine Diagnostic Tools

BACKGROUND

Diabetes and prediabetes are diagnosed differentially by Western and Chinese medicine. While Western medicine uses objective laboratory analysis of biochemical parameters to define the severity of diabetes and prediabetes, Chinese medicine uses a comprehensive approach that integrates observation, inquiry, pulse palpation, and tongue diagnosis. The medical information collected is then categorized into different syndromes. However, traditional methods of pulse and tongue diagnoses used to determine syndrome differentiation are highly subjective and skill dependent.

OBJECTIVE

This study aims to identify the gap in conventional traditional Chinese medicine (TCM) diagnostic techniques for syndrome differentiation analysis using contemporary diagnostic devices. We devised a protocol for a nonrandomized, exploratory, observational case-control study with equal allocations in 5 arms to investigate the syndrome differentiation of diabetes and prediabetes. We hypothesize that the TCM syndrome differentiation of diabetes and prediabetes in the tropical climate may differ from that defined based on the Chinese demographic. We also speculate that the high-frequency spectral energy may reflect a difference in pulse wave intensity and density between the healthy and diabetes groups.

METHODS

A total of 250 eligible participants will be equally assigned to 1 of 5 arms (healthy or subhealthy, prediabetes, diabetes, prediabetes with hypertension and dyslipidemia, and diabetes with hypertension and dyslipidemia). Participants aged 21-75 years, of any sex or race, and have been diagnosed with diabetes (fasting plasma glucose [FPG] of 7 mmol/L, or 2-hour plasma glucose [2hPG] of 11.1 mmol/L) or prediabetes (impaired FPG of 6.1-6.9 mmol/L, or impaired glucose tolerance with an 2hPG of 7.8-11 mmol/L) will be included. The Health Evaluation Questionnaire, Physical Activity Questionnaire, sugar intake assessment, Constitution in Chinese Medicine Questionnaire, radial pulse diagnosis, and tongue diagnosis will be performed in a single visit. ANOVA for continuous data and chi-square tests of independence will be used for categorical data assessments, with a level of P<.05 considered significant.

RESULTS

The recruitment is in progress. We anticipate that the study will conclude in June 2025. As of July 15, 2024, we have enrolled 140 individuals.

CONCLUSIONS

To the best of our knowledge, this is the first study to use contemporary TCM diagnostic instruments to map expert and empirical knowledge of TCM to its scientific equivalents for the purpose of evaluating the syndrome differentiation of diabetes. We designed this protocol with the exploratory goal to examine objectively the syndrome differentiation of patients with diabetes and those with prediabetes using TCM diagnostic technologies. The data collected and evaluated under standardized conditions using these contemporary diagnostic devices will exhibit a higher degree of stability, hence yielding dependable and unbiased results for syndrome differentiation. Thus, our findings may potentially increase the accuracy of identification, diagnosis, treatment, and prevention of diabetes and prediabetes through a system of targeted treatment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05563090; https://clinicaltrials.gov/ct2/show/NCT05563090.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56024.

Diagnostic value of isolated plasma biomarkers and its combination in neurodegenerative dementias: A multicenter cohort study

BACKGROUND

Plasma amyloid-β (Aβ), phosphorylated tau-181 (p-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) potentially aid in the diagnosis of neurodegenerative dementias. We aim to conduct a comprehensive comparison between different biomarkers and their combination, which is lacking, in a multicenter Chinese dementia cohort consisting of Alzheimer's disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP).

METHODS

We enrolled 92 demented patients [64 AD, 16 FTD, and 12 PSP with dementia] and 20 healthy controls (HC). Their plasma Αβ, p-tau181, NfL, and GFAP were detected by highly sensitive-single molecule immunoassays. Αβ pathology in patients was measured by cerebrospinal fluid or/and amyloid positron emission tomography.

RESULTS

All plasma biomarkers tested were significantly altered in dementia patients compared with HC, especially Aβ42/Aβ40 and NfL showed significant performance in distinguishing AD from HC. A combination of plasma Aβ42/Aβ40, p-tau181, NfL, and GFAP could discriminate FTD or PSP well from HC and was able to distinguish AD and non-AD (FTD/PSP).

CONCLUSIONS

Our results confirmed the diagnostic performance of individual plasma biomarkers Aβ42/Aβ40, p-tau181, NfL, and GFAP in Chinese dementia patients and noted that a combination of these biomarkers may be more accurate in identifying FTD/PSP patients and distinguishing AD from non-AD dementia.

The impact of dietary supplements on blood pressure in older adults: A network meta-analysis of randomized controlled trials

Purpose

The prevalence of hypertension (HTN) increases with age and there is a need for effective, evidence-based treatments for HTN among older adults. The objective of this study was to perform a network meta-analysis to evaluate the effectiveness of different forms of nutritional supplementation on reducing blood pressure in older adults.

Methods

A systematic review using PubMed and Clinical Key was performed to identify randomized controlled trials (RCTs) evaluating the effects of dietary supplements on blood pressure in adults older than 65 years of age. Network meta-analysis (NMA) was used to compare and rank the effects of different supplements on systolic (sBP), diastolic (dBP), and mean (mBP) blood pressure. Supplements were ranked according to P score. Meta-regressions were conducted to examine whether treatment effects were moderated by baseline BP and supplementation duration.

Findings

We identified 144 relevant studies in the literature, twelve of which met criteria for inclusion in NMA. The included studies were published between 2003 and 2022. In reducing sBP, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), inorganic nitrates, tart cherry juice, and vitamin D supplementation were more effective than placebo, and the effect of tart cherry juice outranked that of vitamin D, vitamin E, and vitamin K2. In reducing dBP, inorganic nitrates, DHA and EPA, protein, resveratrol, and vitamin D supplementation were more effective than placebo, and the effect of resveratrol outranked that of tart cherry juice, vitamin D, vitamin E, and vitamin K2. However, the effects of tart cherry juice on sBP and resveratrol on dPB were smaller than the pooled effect of placebo, and none of the pairwise differences between the effects of examined supplements were statistically significant. Caution is needed when interpreting these results given concerns about the risk of bias assessed in seven of the twelve studies included in this analysis.

Saliva biomarkers in oral disease

Saliva is a versatile biofluid that contains a wide variety of biomarkers reflecting both physiologic and pathophysiologic states. Saliva collection is noninvasive and highly applicable for tests requiring serial sampling. Furthermore, advances in test accuracy, sensitivity and precision for saliva has improved diagnostic performance as well as the identification of novel markers especially in oral disease processes. These include dental caries, periodontitis, oral squamous cell carcinoma (OSCC) and Sjögren's syndrome (SS). Numerous growth factors, enzymes, interleukins and cytokines have been identified and are the subject of much research investigation. This review highlights current procedures for successful determination of saliva biomarkers including preanalytical factors associated with sampling, storage and pretreatment as well as subsequent analysis. Moreover, it provides an overview of the diagnostic applications of these salivary biomarkers in common oral diseases.

The accuracy of serum osmolarity calculation in small children

Background

Serum osmolality can be measured (Omeas) or calculated (Ocal). Many formulas for Ocal have been already published, but data regarding the most accurate equation in small babies is not available. Thus, we aim to compare Omeas and Ocal obtained by different formulas in newborns and small children.

Methods

The study included 280 serum samples taken from children, from the first day of life to 2 years (mean age 8.2 ± 7.6 months) treated in the University Children's Hospital in Krakow. The serum osmolality was measured by osmometer and calculated by 8 common formulas.

Results

The mean value of Omeas (2 8 5 .8 ± 5 .1 mOsm/kgH2O) was significantly different as compared to the mean values of Ocal (p< 0.01) for all formulas, except Ocal obtained by the formula: 1.86*(N a + K) +1.15*Glu + Urea + 14. According to Bland-Altman analysis, this formula showed the best performance for estimating osmolality. In children under 3 months of life Passing-Bablok regression indicated both systematic and proportional error for results obtained by each formula compared to the measured values.

Conclusions

To calculate osmolarity in children aged between 3 months and 2 years old the following equation: 1 .86*(N a + K) + 1.1 5*G lu+ U rea+ 14 might be used, whereas serum osmolality in children up to 3 month of life should be measured.

Caries-related salivary parameters and oral microbial flora in patients with type 1 diabetes: A meta-analysis

OBJECTIVES

This study aimed to conduct a meta-analysis by synthesising the outcomes of studies that investigated the relationship between type 1 diabetes (T1D) and salivary flow rate (SFR), salivary pH (SpH), salivary buffer capacity (SBC), streptococcus Mutans (SM), and lactobacillus (LB) counts.

MATERIAL AND METHODS

The PRISMA statement guide was followed for the meta-analysis. Electronic databases were searched, and study selection and data collection processes were performed. The risks of bias in individual studies and across studies were assessed. Mean differences (MD) and Odds Ratio (OR) were used to measure the effect estimates in the comparisons.

RESULTS

29 studies were included in the qualitative and quantitative syntheses. Significantly higher SFR (MD = -0.22, CI: -0.26, -0.18; p < 0.001) and SpH (MD = -0.59, CI: -0.81, -0.36; p < 0.001) were observed in the healthy individuals than T1D individuals. No significant difference was observed among groups in terms of SBC (MD = 0.10, CI: -0.46,0.66; p = 0.73). An increased odds ratio of SM counts were observed regarding the T1D (OR = 3.09, 95% CI: 1.16, 8.20; p = 0.02). No association was found between LB counts and T1D (OR = 2.15, 95% CI: 0.38, 11.98; p = 0.38).

CONCLUSIONS

Subjects with T1D have a significantly lesser SFR and SpH than healthy individuals. But no significant difference is available in terms of SBC. Lower SM counts were observed in individuals with T1D, while no association was observed regarding LB counts. The tendency to dental caries is more likely in subjects with T1D due to lower SFR, SpH, and higher SM.

Analysis of conventional and nonconventional forensic specimens in drug-facilitated sexual assault by liquid chromatography and tandem mass spectrometry

The incidence of drug-facilitated sexual assault (DFSA) has dramatically increased in the last decades. Forensic analytical scientists continuously seek new methods and specimens to prove the incidence of intoxication for the judiciary system. Factors influencing sample selection include the ease of obtaining the samples and the window of detection of the drugs, among others. Both conventional (blood, urine) and non-conventional specimens (hair, nails, fluids) have been proposed as suitable in DFSA cases. Reported sample treatments include a variety of liquid-liquid and solid-phase extraction as well as dilute-and-shoot procedures and microextraction techniques. Regarding analysis, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) has emerged as the preferred confirmatory technique, due to its sensitivity, selectivity, and wide-scope applicability. In this review, we critically discuss the most common specimens and sample treatments/analysis procedures (related to LC-MS/MS) that have been reported during the last ten years. As a final goal, we intend to provide a critical overview and suggest analytical recommendations for the establishment of suitable analytical strategies in DFSA cases.

Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection

As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.

Improving the phishing website detection using empirical analysis of Function Tree and its variants

The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist anti-phishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based meta-learning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its meta-learner variants for phishing website detection and applicable cybersecurity attacks are recommended.

Size-dependent light scattering of CoOOH nanoflakes for convenient and sensitive detection of alkaline phosphatase in human serum

As a natural enzyme, alkaline phosphatase (ALP) plays an essential role in clinicopathological examinations and biomedical research, and is capable of hydrolyzing the phosphate group of l-ascorbic acid-2-phosphate (AAP) to yield l-ascorbic acid (L-AA). L-AA reduced cobalt oxyhydroxide (CoOOH) nanoflakes to Co²⁺ , leading to a smaller size and weaker light scattering, which could be monitored by electron microscopic images and optical spectra. The indirect detection of ALP was achieved by the reduced light scattering signal of CoOOH nanoflakes. Under optimal conditions, the decrease in scattering intensity was proportional to the ALP concentration over the range 0.1-160 U/L and the detection limit was 0.034 U/L (3σ/k). Compared with other assays, this proposed light scattering method was more convenient and economic for ALP sensing. The method was successfully applied to ALP analysis in human serum samples, and was similar to the results obtained by commercial kits.

Effects of brain tissue section processing and storage time on gene expression

The pre-processing of samples is important factors that affect the results of the RNA-sequencing (RNA-seq) data. However, the effects of frozen sections storage conditions on the integrity of RNA and sequencing results haven't been reported. The study of frozen section protection schemes can provide reliable experimental results for single-cell and spatial transcriptome sequencing. In this study, RNA was isolated to be studied for RNA from brain section at different temperatures (RT: room temperature, -20 °C) and storage time (0 h, 2 h, 4 h, 8 h, 12 h, 16 h, 24 h, 7day, 3week, 6month). The stability of reference genes was validated using reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that the storage at room temperature significantly affected RNA integrity number (RIN), and the RIN value was lower with the prolongation of storage, while the storage at -20 °C exerted less effect on the RIN value. Cresyl violet staining for brain tissue sections had little effect on RNA integrity. 1925, 899 and 3390 differential expression genes (DEGs) were screened at 2 h, 4 h and 8 h at room temperature, respectively. A total of 892, 478 and 619 genes were shown to be differentially expressed at -20 °C for 7d, 3w and 6 m, respectively. Among them, the expression of glycoprotein m6a (Gpm6a), calmodulin 1 (Calm1), calmodulin 1 (Calm2), thymosin, beta 4, X chromosome (Tmsb4x), ribosomal protein S21 (Rps21) and so on were correlated with RNA quality. According to the expression stability of 4 reference genes (Actb: beta-actin; Gapdh: glyceraldehyde-3-phosphate dehydrogenase; 18S: 18S ribosomal; Hprt1: hypoxanthine phosphoribosyltransferase 1), 18S is the most stable reference gene in the brain. In conclusion, the storage temperature and time of frozen sections have significant effects on RNA integrity and sequencing results. But there are still some genes that are stable and not affected by worsening of overall RNA integrity ie the decrease of RIN value. In addition, 1% cresyl violet staining can protect RNA.

Secretome Analysis of Inductive Signals for BM-MSC Transdifferentiation into Salivary Gland Progenitors

Severe dry mouth in patients with Sjögren's Syndrome, or radiation therapy for patients with head and neck cancer, significantly compromises their oral health and quality of life. The current clinical management of xerostomia is limited to palliative care as there are no clinically-proven treatments available. Previously, our studies demonstrated that mouse bone marrow-derived mesenchymal stem cells (mMSCs) can differentiate into salivary progenitors when co-cultured with primary salivary epithelial cells. Transcription factors that were upregulated in co-cultured mMSCs were identified concomitantly with morphological changes and the expression of acinar cell markers, such as α-amylase (AMY1), muscarinic-type-3-receptor(M3R), aquaporin-5(AQP5), and a ductal cell marker known as cytokeratin 19(CK19). In the present study, we further explored inductive molecules in the conditioned media that led to mMSC reprogramming by high-throughput liquid chromatography with tandem mass spectrometry and systems biology. Our approach identified ten differentially expressed proteins based on their putative roles in salivary gland embryogenesis and development. Additionally, systems biology analysis revealed six candidate proteins, namely insulin-like growth factor binding protein-7 (IGFBP7), cysteine-rich, angiogenetic inducer, 61(CYR61), agrin(AGRN), laminin, beta 2 (LAMB2), follistatin-like 1(FSTL1), and fibronectin 1(FN1), for their potential contribution to mMSC transdifferentiation during co-culture. To our knowledge, our study is the first in the field to identify soluble inductive molecules that drive mMSC into salivary progenitors, which crosses lineage boundaries.