Normal Variation Research Articles

FORDYCE GRANULES AS A POTENTIAL CHAIRSIDE CLINICAL PREDICTOR FOR HYPERCHOLESTEROLEMIA: A CROSS-SECTIONAL STUDY

Background: Fordyce’s granules (FGs) are ectopic sebaceous glands commonly located in the oral cavity, appearing most frequently on the buccal mucosa, vermilion border of the lips, and occasionally in the retromolar areas. Owing to its lipid content, there has been conjecture regarding a potential connection between FGs and dyslipidemia, a disorder characterized by elevated lipid levels that may be linked to ectopic lipid deposits in different tissues. Aim: The purpose of this cross-sectional study was to ascertain whether Fordyce's granules could be used as a chair side clinical marker to identify those who are at risk for dyslipidemia. Methods: The cross-sectional study was carried out at Saveetha Dental College and Hospitals in Chennai over the course of a year, from June 2023 to May 2024. 100 adult dental patients between the ages of 18 and 60 were examined clinically by an oral medicine specialist to determine whether Fordyce granules were present. Every participant had their blood drawn in order to determine their lipid levels. Analysis was performed on the data to determine predictors for hypercholesterolemia. Results: Patients with detectable oral FG had a mean age of 50.2 ± 12.3 years. A mean total cholesterol of 250.3 ± 41.7 mg/dL (p <0.001) and mean HDL cholesterol value of 42.0 ± 8.1 mg/dL (p < 0.05) were seen to be associated with presence of FG with statistical significance. The odds ratio (1.083) indicates that for every unit increase in TC, the odds of FG increase by 8.3%. Conclusion: The presence of oral Fordyce granules may indicate increased total cholesterol predisposing individuals to hypercholesterolemia. Factors such as age, smoking, and diabetes mellitus showed no association between Fordyce granules and lipid profiles.Previously regarded as a normal anatomical variation, fordyce granules should be considered in conjunction with other risk factors to identify possible dyslipidemia.

Associations among externalizing psychopathology, personality, and behavioral traits: Models of an externalizing spectrum in youth.

Consistent evidence has documented the cross-sectional and longitudinal associations of externalizing psychopathology with personality and behavioral traits, suggesting the presence of a broad, underlying liability to externalizing. In one of the first studies of its kind, we use a large, representative sample of youth (N = 2,245 twins and their siblings) to evaluate the evidence of an externalizing spectrum model, which includes psychopathology, personality, and behavioral traits and spans normal and pathological variation. We examine evidence for the inclusion of 15 candidate traits, from the domains of general and pathological personality, temperament, and aggression, in a model that includes dimensions of common childhood externalizing psychopathology. Using a combination of structural equation modeling and item response theory analyses, we found strong to moderate evidence for including the narcissism and impulsivity dimensions of psychopathic traits; reactive, proactive, and relational aggression; and agreeableness and conscientiousness from the five-factor model of personality. These traits were reliable indicators of the externalizing spectrum, as evidenced by their shared variance with externalizing symptoms, strong factor loadings, and high information. In addition, these traits indexed the externalizing spectrum at higher and lower levels of the latent trait relative to the symptoms alone, highlighting the value of including them. Many of our findings replicate and extend work conducted in adult samples, suggesting developmental continuity of externalizing. Broadly speaking, these findings have important implications for the conceptualization, measurement, and treatment of externalizing in youth. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Asymptomatic neck mass and normal variations of the thymus in children: A report of two cases from a tertiary hospital in Riyadh, Saudi Arabia

The pathogenesis of cervical extension of the thymus has been postulated to be incomplete descent of the thymus in the midline of the neck, which is considered normal in children. The thymus normally extends to the neck in approximately two-thirds of children. Ectopic thymus mainly occurs in 1%–13% of the younger population. In this study, we report two cases of asymptomatic neck swelling, one due to cervical extension of the thymus and the other due to a right submandibular ectopic thymus. None of the patients required any intervention. Recurrent suprasternal swelling in children that becomes more pronounced during coughing/crying or Valsalva maneuvers can be more accurately diagnosed using neck ultrasonography and/or magnetic resonance imaging.

Comparison of data augmentation and classification algorithms based on plastic spectroscopy.

Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms. To address this issue, we propose a plastic spectroscopy generation model and conduct a systematic analysis and comparison of different algorithms' performance from multiple perspectives, based on data augmentation. This paper first performs cubic interpolation, normalization, S-G filtering, linear detrending, and standard normal variate (SNV) transformations as preprocessing methods on plastic spectral data collected from public datasets using techniques such as Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy (RAMAN), and Laser Induced Breakdown Spectroscopy (LIBS). The results, based on Principal Component Analysis (PCA) visualization, demonstrate that the preprocessing steps help improve classification accuracy. Additionally, PCA loading is used to explain the chemical classification features of each spectral device. Secondly, to tackle the issue of insufficient sample size, we propose a plastic spectroscopy generation model based on C-GAN, which effectively handles multi-class spectroscopy generation. The generated spectra are subjectively validated through difference spectroscopy and t-SNE to confirm their consistency with real spectra, and this conclusion is objectively validated using Maximum Mean Discrepancy (MMD). Finally, we compared the classification accuracy of machine learning algorithms, including Support Vector Machine (SVM), Back Propagation Neural Network (BP), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), with deep learning algorithms such as GoogleNet and ResNet under various conditions. The results indicate that after data augmentation using the plastic spectrum generation model, the accuracy of each classification model improved by at least 3% compared to pre-augmentation levels. Notably, for data collected via FTIR, the classification accuracy reached a peak of 0.991 under the 1D-ResNet model when the data were augmented twofold. For small sample datasets, traditional machine learning algorithms, such as SVM and RF, demonstrated high stability and accuracy, with only minimal differences compared to deep learning algorithms. However, on large sample datasets, deep learning algorithms showed a stronger advantage. Regarding data input formats, 1D input models generally outperformed 2D input models. Grad-CAM visualizations further illustrated that the 1D-ResNet model achieved the highest classification accuracy, primarily due to its ability to more accurately identify peak features in the data.

Artificial intelligence with wrapper multioutput regression to identify chemical contents of fresh and dried manure samples using NIR spectroscopy.

Analyzing manure nutrients such as total ammonium nitrogen (NH+4), dry matter (DM), calcium oxide (CaO), total nitrogen (-N), phosphorus pentoxide (P2O5), magnesium oxide (MgO), and potassium oxide (K2O) helps in fulfilling crop nutritional needs while improving the profitability and a lower risk of pollutants. This study used two Near Infra Red (NIR) spectral datasets of fresh and dried manure. The freshly prepared NH4Cl, CaO, Ca(OH)2, P2O5, MgO, and K2O samples were used for spectral signature peak identification and calibration. The pre-processing methods, Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), were applied to correct the spectra and PCA to reduce the dimension of these pre-processed spectra. Several regressors and classifiers with wrapper multioutput regression algorithms (WMRA) and stacking generalization models were applied to identify the chemical contents. In the regression analysis, the WMRA achieved the highest coefficient of determination (R2) of 0.983 and RPD of 7.68 for fresh manure, while R2 of 0.949 and RPD of 4.42 for dried manure. In classification analysis, k-nearest neighbors (KNN) achieved 100.0% highest accuracy, while 98.1% 5-fold average accuracy. The overall findings of this study indicate that NIR, in conjunction with WMRA and KNN, has a high potential for quick detection of nutrients in manure.

Frequency Of Normal Variation Of Buccal And Labial Mucosa In Oral Disease Clinic Rsgm Andalas University

Introduction: Diseases or lesions of the oral mucosa are a significant aspect of oral diseases that greatly affect the patients experiencing them. Among all oral mucosal diseases, there are several conditions categorized as normal variations in the anatomical structure of the oral mucosa. Normal variations in the oral cavity are clinical presentations that are unusual. Non-pathogenic conditions, namely clinical variations in the form of uncommon organ structures with specific characteristics, fall into the category of normal and are not pathological conditions. Material and Method: This study employed observational descriptive research to examine the presentation of normal variations in the oral mucosa of patients at the oral disease clinic of the Faculty of Dentistry, Andalas University. Univariate analysis was conducted in this research to observe the distribution of normal variations in the oral mucosa. Results and Discussion: The number of samples in this study was 620 patient data, with the prevalence of normal variations in the oral mucosa found to include cheek biting, fordyce granule, linea alba, leukoderma, and irritation fibroma. The highest prevalence was cheek biting at 52.6%, while the lowest was irritation fibroma at 1.8%. Conclusion: Cheek biting is the most common normal variation in the oral mucosa observed at the oral disease clinic of the Faculty of Dentistry, Andalas University.

Association of hemoglobin levels with metabolic traits in women with PCOS.

Within normal variation, higher hemoglobin (Hb) levels are associated with poorer metabolic profile in population cohorts, underlying the link between oxygen delivery and cell metabolism. Polycystic ovary syndrome (PCOS) is the most common endocrine disorder among women and is commonly accompanied by metabolic derangements. In this study we sought to investigate Hb levels, and their metabolic associations, in women with PCOS. We used data from Northern Finland Birth Cohort 1966 to evaluate Hb levels in women with or without PCOS at the ages of 31 and 46 years. Linear regression models were used to investigate associations between Hb levels and essential metabolic parameters in both groups. Women with PCOS had higher Hb levels than controls at the age of 31 years but not at the age of 46 years. Hb levels were associated positively with most of the metabolic parameters tested (body mass index, waist circumference, fasting insulin, homeostatic model assessment-insulin resistance (HOMA-IR), blood pressure, inflammatory markers, and blood lipids), with stronger associations in women with PCOS than in non-PCOS controls. There were fewer associations at the age of 46 than at 31 years, and body mass index seemed to explain many, though not all, differences between the PCOS and non-PCOS groups. Women with PCOS have higher Hb levels at the age of 31 years. In both women with and without PCOS, Hb levels associate with poorer metabolic profile.

The complex anatomy of the bronchial arteries: a meta-analysis with potential implications for thoracic surgery and hemoptysis treatment

The present meta-analysis aimed to provide the most detailed and comprehensive anatomical description of bronchial arteries (BAs) using data available in the literature. Adequate knowledge of the normal anatomy and morphological variations of BAs can be clinically significant; for example, this approach can prevent potential risks while undertaking bronchial artery embolization (BAE) procedures and, ultimately, lead to better patient outcomes. Major medical databases such as PubMed, Scopus, Embase, Web of Science, Google Scholar, and the Cochrane Library were searched. The overall search process was conducted in three main stages. The number of BAs varied from one to six, and 16 arterial patterns were observed. The most common variation was in one right BA and one left BA, with a pooled prevalence of 19.54% (95% CI 6.69–36.44%). The pooled prevalence of BAs originating separately from the aorta was 41.42% (95% CI 37.42–45.48%). The number and location of BAs are highly inconsistent. However, the most prevalent pattern involved two BAs: one in the right BA and one in the left BA. Although BAs most frequently originate from the descending aorta, the cooccurrence of at least one ectopic BA is relatively high. The results of our meta-analysis can serve as a source of comprehensive information for thoracic surgeons and physicians performing endovascular procedures, especially BAE, a treatment for life-threatening hemoptysis.

Exploring Pediatric Sutural Variations with 3D CT Imaging: A Retrospective Study at a Tertiary Hospital.

Sutural anatomy variation has long been a topic of debate among anatomists, paleontologists, and morphologists. While the exact reasons for the prevalence of this variance remains a topic of ongoing discussion, developmental and genetic factors are hypothesized to be the main reasons. Understanding the morphology and occurrence of normal sutural variations in pediatric patients is essential to making the right diagnosis, where a misinterpretation of a sutural bone may lead to an inaccurate assessment, completely misleading the diagnostic process. This is a retrospective study, we collected the data of the last 100 pediatric patients under the age of 29 months who underwent a 3D Skull CT for reasons of trauma. Our study, examining pediatric patients who underwent 3D CT imaging, consisted of 100 children, divided by gender as 52% females and 48%. The overall mean age of the cohort was 10.04 months. The anterior fontanelle was closed in 28% (n=28). The posterior fontanelle was closed in 84% with a mean age of 11.57 months (SD=6.24). Notably, all participants had patent coronal, lambdoid, and squamosal sutures (n=100, 100%) with a mean age of 10.04 months (SD = 6.8). The metopic suture was fused in 70% of the participants (n = 70). A metopic ridge was present in 22% of the participants. Wormian bones were present in 59% (n = 59) of the participants, the majority being on the lamboid suture. We also found other multiple cranial bone variations among our pediatric patients. Involving the spheno-parietal suture, spheno-frontal, posterior intraoccipital suture, frontozygomatic suture, and the mendosal suture. The current study provides valuable insights into the prevalence and characteristics of skull suture variations in the Saudi pediatric population under the age of one, based on 3D CT imaging data. Our findings indicate a notable occurrence of sutural bone variations, including the presence of Wormian bones and different patterns of suture patency.

Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots

Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.

Ossification patterns of the C1 (atlas) and C2 (axis) vertebrae children.

This retrospective study aimed to assess ossification patterns and synchondrosis fusion timelines of the C1 and C2 vertebrae in pediatric age group, to help differentiate injuries from normal variations and serve as a guide when evaluating incompletely fused synchondrosis. The study analyzed 432 CT examinations of children aged 0-72months, conducted at a single institution between January 2010 and January 2018. The focus was assessment of the visibility and fusion of the three ossification centers and three synchondroses of the atlas, and six ossification centers and four synchondroses of the axis, based on age. Complete ossification of the anterior arch of the atlas was observed in 60.4% of patients, increasing with age from 11.1% in the 0-12months age group to 97.3% in the 61-72months age group. Patency of the ventrolateral synchondrosis of the atlas decreased from 100% in the 0-12months age group to around 30% in the 61-72months age group. Subdental synchondrosis and neurocentral synchondrosis of the axis were patent in 47.0% and 50.6% of patients, respectively, both decreasing with age. The apicodental synchondrosis of the axis remained mostly patent (98.1%). Overall, there was a trend of decreasing patency in synchondroses and increasing ossification with advancing age. These findings provide a better understanding of normal ossification patterns and timelines, facilitating the accurate distinction between normal variations and traumatic injuries.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

Aberrant right subclavian artery: the importance of distinguishing between isolated and non-isolated cases in prenatal diagnosis and clinical management.

This study aimed to evaluate the association ofaberrant right subclavian artery (ARSA) with genetic abnormalities and postnatal symptomatology, by comparing isolated and non-isolated ARSA cases. Retrospective, descriptive and comparative study involving fetuses diagnosed with ARSA during routine fetal ultrasound scans, between 19 and 40weeks, in a tertiary referral university hospital in Barcelona from January 2007 to December 2023. Out of 154 fetuses diagnosed with ARSA, 75.3 % (116) were classified as isolated cases, while 24.7 % (38) had associated structural anomalies. Non-invasive prenatal testing with cell-free DNA was performed in 27.3 % (42) of cases, yielding low-risk results in 96.6 %, with only one case of trisomy 21 detected. Genetic evaluation was pursued in 15.6 % (24) of cases, revealing abnormalities in three fetuses: one with trisomy 21 and two with 22q11 deletion. All affected fetuses had associated structural defects. Overall, 151 fetuses survived after birth, there were two terminations of pregnancy and one stillbirth. Mild symptoms at birth were observed in 1.9 % (3) of newborns, all from the non-isolated ARSA group. These findings emphasize the significance of distinguishing between isolated and non-isolated ARSA cases in prenatal diagnosis and clinical management, suggesting that isolated ARSA may be considered a normal vascular variation.

基于高光谱成像的大豆籽粒含水率检测研究

Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

Shotgun metabolomic analysis of killer whale (Orcinus orca) exhaled breath condensate.

The ocean is facing many anthropogenic stressors caused from both pollution and climate change. These stressors are significantly impacting and changing the ocean's ecosystem, and as such, methods must continually be developed that can improve our ability to monitor the health of marine life. For cetaceans, the current practice for health assessments of individuals requires live capture and release, which is expensive, usually stressful, and for larger species impractical. In this study, we investigated the potential of exhaled breath condensate (EBC) samples to provide unique metabolomic profiles from healthy killer whales (Orcinus orca) of varying known age and sex. EBC collection is a non-invasive procedure that has potential for remote collection using unmanned aerial vehicles, thus improving our ability to understand physiologic parameters within wild populations while minimizing stress from collection procedures. However, descriptions of the available metabolome within EBC and its clinical significance within animals of known health and age must be described before this technique can be considered diagnostically useful. We describe normal variations of the metabolome across age and sex and provide evidence for the potential of this breath analysis method to become a valuable adjunctive tool for assessing the health of managed-care and free-ranging killer whales.

The evolution of a placenta accelerates the evolution of post-copulatory reproductive isolation.

The evolution of placentation is predicted to intensify intergenomic conflicts between mothers and offspring over optimal levels of maternal investment by providing offspring opportunities to manipulate mothers into allocating more resources. Parent-offspring conflicts can result in the evolution of reproductive isolation among populations when conflicts resolve in different ways. Postzygotic reproductive isolation is hypothesized to evolve more rapidly following the evolution of placentation due to the predicted increase in conflict. We tested this hypothesis by performing interpopulation crosses within placental and non-placental species of Poeciliopsis to determine if the relationship between genetic distance and measures of postzygotic reproductive success differed as function of reproductive mode. We did not observe any differences in offspring viability or sterility among crosses. Offspring size declined rapidly as a function of interpopulation genetic distance within the placental species, but not among our non-placental species. The decrease in offspring size in the placental species was beyond normal variation, likely representing a major fitness cost, consistent with the prediction that negative epistatic interactions are evolving more quickly among populations in our placental species than the non-placental species. We discuss how our results support the role parent-offspring conflicts play in the evolution of reproductive isolation and reproductive mode.

Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging

In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production.

Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis.

The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including 'Sucui No.1', 'Zaojinxiang', 'Huangguan', 'Akizuki', 'Yali', and 'Hongli No.1'. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.

Variations in Cystic Duct Anatomy Using Magnetic Resonance Cholangiopancreatography and their Clinical Significant in the Sudanese

Background: Anatomical variations of the cystic duct are very frequent, and it is substantial to be familiar with these variations preceding to any surgical, percutaneous and endoscopic intervention procedures.Objective: The aim of this study is to determine the normal (typical) and atypical anatomical variations in the cyst duct using magnetic resonance cholangiopancreatography at Fedail Specialized Hospital in Khartoum, Sudan.Materials and Methods: This study is a retrospective evaluation of the cystic duct in 307 patients who were routinely imaged with Magnetic Resonance cholangiopancreatography between November 2019 and March 2021 for various indications.Result: This study included 307 patients aged 6 months to 100 years, of whom 44.3% were male and 55.7% were female. The location, level, and course of the cystic duct were examined. The typical anatomy of the cystic duct was seen in only 30.29% of patients; this percentage is low compared to the range reported in the literature (58–75%). A total of 69.71.% showed atypical configurations. The most common variant was high cystic insertion, observed in 36.2% of cases. Low insertion was observed in 21.8%, low medial insertion in 14.6%, a spiral posterior course in 15.6%, and a parallel course in 10.1%. A short cystic duct less than 5 mm in length was seen in 1% of cases. Other rare findings include a case-for-case (0.3%): absent cystic duct; spiral anterior course with low medial insertion; spiral anterior then posterior; drainage of cystic duct to the right hepatic duct; drainage of the cystic duct at the junction between the right and left hepatic ducts; very high insertion just below the junction of the right and left hepatic ducts; and finally, a case of common bile duct cyst. The relationship between the location, height, and course of the cystic duct and gender was examined, and the result of the chi-square test showed that there was no statistically significant difference between males and females with regard to anatomical variations (p = 0.740). (p=0.671) (p=0.678).Conclusion: The normal anatomy of the cystic duct is observed in only 30.29% of cases. Anatomical variations are encountered in 69.71% of cases, with high insertion of the cystic duct being the main variation observed in 36.2% of cases; the next most common variation is a low insertion in 21.8% of cases.

Sublingual varices as predictor of factors associated with cardiovascular diseases: A systematic review with meta-analysis.

Sublingual varicose veins are variations of normality that appear more prominent during aging. The systematic review aim to investigate sublingual varicose veins frequency in patients exposed to systemic and environmental exposed factors linked to cardiovascular diseases. This systematic review was based on the PECO strategy, and the PubMed, Web of Science, Scopus, Embase and Grey Literature library were searched for publications examining the association between sublingual varicose veins and systemic and environmental exposed factors linked to cardiovascular diseases, such as hypertension, diabetes, smoking, and leg varices. A meta-analysis was conducted using random-effects models. This review included 11 studies with a total of 2,272 patients with sublingual varicosity and systemic exposed factors linked to cardiovascular diseases. The quality assessment was improved by Newcastle and Ottawa Scales (NOS). Consistent results were observed across all systemic and environmental exposed factors linked to cardiovascular diseases subgroups. We found association between sublingual varicose veins and hypertension [p = 0.0002; 95%IC2.90 [1.66-5.06]; I2 91%); diabetics (p = 0.009; 95%IC 3.19 [1.34 - 7.62]; I2 84%); tobacco abuse (p = 0.002; 95%IC 1.97 [1.29 - 3.01]; I2 82%) and leg varicosity (p = 0.03; 95%IC 2.63 [1.08 - 6.44]; I2 95%). Seven articles received score 7, indicating good quality and four articles received moderate quality. Patients exposed to exposed factors that affect blood vessels showed a higher prevalence of sublingual varicose veins. These findings emphasize the importance of recognizing oral signs underlying systemic diseases. PROSPERO CRD42023397166.