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BACKGROUND: Non-Asian body mass index (BMI) classifications are commonly used as a risk factor for high fasting blood glucose (FBG). We investigated the incidence and factors associated with high FBG among people living with HIV in the Asia-Pacific region, using a World Health Organization BMI classification specific to Asian populations. METHODS: This study included people living with HIV enrolled in a longitudinal cohort study from 2003 to 2019, receiving antiretroviral therapy (ART), and without prior tuberculosis. BMI at ART initiation was categorized using Asian BMI classifications: underweight (25 kg/m2 were at increased risk of high FBG. This indicates that regular assessments should be performed in those with high BMI, irrespective of the classification used.

In order to rapidly and non-destructively detect gastrodin and 4-hydroxybenzyl alcohol in Rhizoma Gastrodiae, near infrared spectral data of the dried tuber of Gastrodia elata Bl. f. glauca S. Chow were collected in the wavelength range of 900–1 700 nm. First, convolutional smoothing (SG) and standard normal variable transformation (SNV) were used for spectral data preprocessing. Second, feature wavelength extraction was carried out by competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). According to the results of establishing least squares support vector machine (LSSVR) model based on feature wavelength, the best method of feature wavelength extraction was selected. In order to improve the accuracy of the model, this study introduced the artificial rabbits optimization (ARO) algorithm to optimize the regularization parameter γ and the kernel function density σ2 in LSSVR and the superiority of ARO to particle swarm optimization (PSO) and grey wolf optimizer (GWO) was evaluated. The results showed that the ARO algorithm was superior to PSO and GWO in in terms of optimization speed and ability. The best prediction models for gastrodin and 4-hydroxybenzyl alcohol were CARS-ARO-LSSVR, with prediction correlation coefficient (R2p) of 0.969 6 and 0.957 7, and root mean square error of prediction (RMSEP) of 0.014 and 0.020, respectively. Therefore, this study shows that near-infrared spectroscopy can be used for quantitative detection of active components in Rhizoma Gastrodiae, which provides a theoretical basis for the development of rapid detection devices for Rhizoma Gastrodiae.

In order to identify Panax notoginseng powders from different root parts, an electronic nose and gas chromatography-mass spectrometry (GC-MS) were used to analyze the volatile components of the whole root powder, rhizome powder, taproot powder, lateral root powder and fibrous root powder of P. notoginseng. The data obtained were analyzed by multiple comparison. The statistical learning method was used to extract eight time-domain features from the response curves of the electronic nose, and correlation analysis was carried out. Three feature selection algorithms were used to reduce the dimension of the feature data. Classification models were built using support vector machine (SVM), least square support vector machine (LSSVM) or extreme learning machine (ELM) based on the original feature data or the three kinds of feature selection data. The grey wolf optimization (GWO) algorithm was introduced to optimize the parameters gam and sig2 in the classification model. The results showed that a total of 31 volatile compounds were detected in the five P. notoginseng powders. The best GWO-IRIV-LSSVM model could effectively distinguish the electronic nose data, with 97.5% accuracy for the test set. Moreover, the volatile composition of the five samples differed mainly in terms of the contents of total volatiles, alkanes, and aromatic compounds, which was consistent with the results of GC-MS. The method used in this study can be used for the detection of high-quality P. notoginseng powder from geo-authentic production areas mixed with low-quality P. notoginseng powder.

IntroductionSince 2015, the World Health Organization (WHO) has recommended that all people living with HIV (PLHIV) initiate antiretroviral treatment (ART), irrespective of CD4+ count or clinical stage. National adoption of universal treatment has accelerated since WHO's 2015 "Treat All" recommendation; however, little is known about the translation of this guidance into practice. This study aimed to assess the status of Treat All implementation across regions, countries, and levels of the health care delivery system.MethodsBetween June and December 2017, 201/221 (91%) adult HIV treatment sites that participate in the global IeDEA research consortium completed a survey on capacity and practices related to HIV care. Located in 41 countries across seven geographic regions, sites provided information on the status and timing of site-level introduction of Treat All, as well as site-level practices related to ART initiation.ResultsAlmost all sites (93%) reported that they had begun implementing Treat All, and there were no statistically significant differences in site-level Treat All introduction by health facility type, urban/rural location, sector (public/private) or country income level. The median time between national policy adoption and site-level introduction was one month. In countries where Treat All was not yet adopted in national guidelines, 69% of sites reported initiating all patients on ART, regardless of clinical criteria, and these sites had been implementing Treat All for a median period of seven months at the time of the survey. The majority of sites (77%) reported typically initiating patients on ART within 14 days of confirming diagnosis, with 60% to 62% of sites implementing Treat All in East, Southern and West Africa reporting same-day ART initiation for most patients.ConclusionsBy mid- to late-2017, the Treat All strategy was the standard of care at almost all IeDEA sites, including rural, primary-level health facilities in low-resource settings. While further assessments of site-level capacity to provide high-quality HIV care under Treat All and to support sustained viral suppression after ART initiation are needed, the widespread introduction of Treat All at the service delivery level is a critical step towards global targets for ending the HIV epidemic as a public health threat.

Sugarcane is a crop that propagates through seed sprouts on nodes. Accurate identification of sugarcane seed sprouts is crucial for sugarcane planting and the development of intelligent sprout-cutting equipment. This paper proposes a sugarcane seed sprout recognition method based on the YOLOv8s model by adding the simple attention mechanism (SimAM) module to the neck network of the YOLOv8s model and adding the spatial-depth convolution (SPD-Conv) to the tail convolution part. Meanwhile, the E-IoU loss function is chosen to increase the model's regression speed. Additionally, a small-object detection layer, P2, is incorporated into the feature pyramid network (FPN), and the large-object detection layer, P5, is eliminated to further improve the model's recognition accuracy and speed. Then, the improvement of each part is tested and analyzed, and the effectiveness of the improved modules is verified. Finally, the Sugarcane-YOLO model is obtained. On the sugarcane seed and sprout dataset, the Sugarcane-YOLO model performed better and was more balanced in accuracy and detection speed than other mainstream models, and it was the most suitable model for seed and sprout recognition by automatic sugarcane-cutting equipment. Experimental results showed that the Sugarcane-YOLO achieved a mAP50 value of 99.05%, a mAP72 value of 81.3%, a mAP50-95 value of 71.61%, a precision of 97.42%, and a recall rate of 98.63%. [ABSTRACT FROM AUTHOR]

Star anise, a widely popular spice, benefits from classification that enhances its economic value. In response to the low identification efficiency and accuracy of star anise varieties in the market, as well as the scarcity of related research, this study proposes an efficient identification method based on non-similarity augmentation and a lightweight cascaded neural network. Specifically, this approach utilizes a Siamese enhanced data network and a front-end SRGAN network to address sample imbalance and the challenge of identifying blurred images. The YOLOv8 model is further lightweight to reduce memory usage and increase detection speed, followed by optimization of the weight parameters through an extended training strategy. Additionally, a diversified fusion dataset of star anise, incorporating open data, was constructed to further validate the feasibility and effectiveness of this method. Testing showed that the SA-SRYOLOv8 detection model achieved an average detection precision (mAP) of 96.37%, with a detection speed of 146 FPS. Ablation experiment results showed that compared to the original YOLOv8 and the improved YOLOv8, the cascade model's mAP increased by 0.09 to 0.81 percentage points. Additionally, when compared to mainstream detection models such as SSD, Fast R-CNN, YOLOv3, YOLOv5, YOLOX, and YOLOv7, the cascade model's mAP increased by 1.81 to 19.7 percentage points. Furthermore, the model was significantly lighter, at only about 7.4% of the weight of YOLOv3, and operated at twice the speed of YOLOv7. Visualization results demonstrated that the cascade model accurately detected multiple star anise varieties across different scenarios, achieving high-precision detection targets. The model proposed in this study can provide new theoretical frameworks and ideas for constructing real-time star anise detection systems, offering new technological applications for smart agriculture. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Data on HIV treatment outcomes in people who inject drugs (PWID) in the Asia-Pacific are sparse despite the high burden of drug use. We assessed immunological and virological responses, AIDS-defining events and mortality among PWID receiving antiretroviral therapy (ART). METHODS: We investigated HIV treatment outcomes among people who acquired HIV via injecting drug use in the TREAT Asia HIV Observational Database (TAHOD) between January 2003 and March 2019. Trends in CD4 count and viral suppression (VS, HIV viral load RESULTS: Of 622 PWID from 12 countries in the Asia-Pacific, 93% were male and the median age at ART initiation was 31 years (IQR, 28 to 34). The median pre-ART CD4 count was 71 cells/[micro]L. CD4 counts increased over time, with a mean difference of 401 (95% CI, 372 to 457) cells/[micro]L at year-10 (n = 78). Higher follow-up HIV viral load and pre-ART CD4 counts were associated with smaller increases in CD4 counts. Among 361 PWID with [greater than or equal to]1 viral load after six months on ART, proportions with VS were 82%, 88% and 93% at 2- 5- and 10-years following ART initiation. There were 52 new AIDS-defining events and 50 deaths during 3347 person-years of follow-up (PYS) (incidence 3.05/100 PYS, 95% CI, 2.51 to 3.70). Previous AIDS or TB diagnosis, lower current CD4 count and adherence CONCLUSIONS: Despite improved outcomes over time, our findings highlight the need for rapid ART initiation and adherence support among PWID within Asian settings. Keywords: people who inject drugs; treatment outcomes; CD4 recovery; viral suppression; tuberculosis; HIV/AIDS; Asia-Pacific, 1 | INTRODUCTION Globally, injecting drug use is a major public health concern leading to substantial health burden and transmission risk of blood-borne infections from injecting equipment sharing behaviours. In [...]

Antiretroviral therapy (ART) is an effective treatment for people living with HIV (PLHIVs), requiring an extended period to achieve immune reconstitution. Metabolic alterations induced by ART are crucial for predicting long‐term therapeutic responses, yet comprehensive investigation through large‐scale clinical studies is still lacking. Here, we collected plasma samples from 108 PLHIVs to the untargeted plasma metabolomics study, based on the longitudinal metabolomics design. Cross‐sectional analyzes were performed at pre‐ and post‐ART to explore the metabolic transformation induced by the therapy. Subsequently, delta values between pre‐ and post‐ART measurements were calculated to quantify metabolic alterations. Then, the optimal set of metabolic traits and clinical signatures were further identified and applied to construct random forest model for predicting the future therapeutic responses to ART. We found distinct ART‐induced metabolic transformation among PLHIVs. After confounder‐adjustments, five metabolites exhibited significant associations with future immune response: tetracosatetraenoic acid (24:4n‐6) (pre‐ART) (odds ratio [OR]: 0.978, 95% confidence interval [CI]: 0.955~0.997), 1‐(3,4‐dihydroxyphenyl)−5‐hydroxy‐3‐decanone (pre‐ART) (OR: 1.298, 95% CI: 1.061~1.727), beta‐PC‐M6 (change) (OR: 0.967, 95% CI: 0.938~0.993), d‐Galactaro‐1,4‐lactone (change) (OR: 1.032, 95% CI: 1.007~1.063), Annuionone C (change) (OR: 1.100, 95% CI: 1.030~1.190). The addition of plasma metabolites to clinical markers accurately predicted immune response to ART with an area under curve of 0.91. Notably, most disrupted metabolites were significantly correlated with blood lipids, suggesting that metabolic transformation might contribute to dyslipidemia among PLHIVs. This study highlights the distinct metabolic transformation post‐ART among PLHIVs and reveals the potential role of metabolic transformation as key determinants of ART efficacy. [ABSTRACT FROM AUTHOR]

The direct ortho‐alkynylation of substituted phenylalanine has been successfully achieved through methoxyiminoacyl (MIA)‐mediated Pd‐catalyzed C−H functionalization. This innovative protocol showcases the ability to apply a diverse range of phenylalanine substrates, resulting in the efficient synthesis of alkynylation benzylamine derivatives with notable effectiveness. Computational investigations further revealed that the fluoride ion enhances the electropositivity of Pd(IV) and the interaction between C1 and C2 within the transition state of the reductive elimination, which significantly expedites the reductive elimination step in the alkynylation process. [ABSTRACT FROM AUTHOR]