High-amylose starch-based gel as green adhesive for plywood: Adhesive property, water-resistance, and flame-retardancy.

The escalating demand for environmentally sustainable and cost-effective adhesives in the wood processing and manufacturing sector has prompted exploration into innovative solutions. This study introduces a novel gel adhesive composed of chemically unmodified high-amylose starch (G70, with 68 % amylose content) with a minimal proportion of urea-formaldehyde (UF) (UF/starch = 1:10, w/w). This G70/UF gel demonstrates remarkable adhesive capabilities for wooden boards under both dry conditions (with a shear stress of 4.13 ± 0.12 MPa) and wet conditions (with a shear strength of 0.93 ± 0.07 MPa after 2 h of water soaking). The study unveils that the elevated amylose content in the starch, coupled with a meticulously controlled isothermal process during bonding, is crucial for these enhancements. Specifically, the robust cohesion of amylose chains expedites phase separation between starch and UF, while the isothermal process facilitates the migration and enrichment of UF molecules at the gel-board and gel-air interfaces. Lacking these mechanisms, conventional amylopectin-rich starch/UF gels (27 % amylose content) show minimal improvement. Moreover, the G70/UF gel showcases exceptional fire retardancy. In all, the G70/UF gel presents a promising alternative for plywood production, reducing reliance on unhealthy UF resin while offering satisfactory bonding resistance in diverse conditions and superior flame retardancy.

Mechanical and modal analysis of different implant strategies for loss of three teeth with bone atrophy in the maxillary posterior region.

PURPOSE: This study aimed to evaluate the stress distribution and secondary stability involved in five implant strategies, including implant-supported prostheses (ISP) and tooth-implant-supported prostheses (TISP), used for bone atrophy in the maxillary posterior region with teeth loss using finite element analysis, and to explore the more desirable implant methods. METHODS: Five implant strategies were made to analyze and compare: M1, implant-supported prosthesis consisting of a short implant with a regular implant; M2, implant-supported prosthesis consisting of a tilted implant with a regular implant; M3, cantilever structure; M4, tooth-implant-supported prosthesis consisting of a short implant with a regular implant; M5, tooth-implant-supported prosthesis consisting of a regular implant, and M6, with only the natural teeth as a control group. Dynamic loading of the above models was performed in finite element analysis software to assess the stress distribution of the bone tissue and implants using the von Mise criterion. Finally, the secondary stability of different models was evaluated by modal analysis. RESULTS: The maximum stress distribution in the cortical bone in M1(60 MPa) was smaller than that in M2(97 MPa) and M3(101 MPa), The first principal strain minimum was obtained in M2 (2271µÎµ). M4 (33 MPa, 10085 Hz) with the best mechanical properties and highest resonance frequency. But increased the loading on the natural teeth. CONCLUSIONS: Short implants and tilted implants are both preferred implant strategies, if cantilever construction is necessary, a tooth-implant-supported prosthesis consisting of a short implant and a regular implant is recommended.

Effect of κ-carrageenan on the quality of crayfish surimi gels.

The demand for crayfish surimi products has grown recently due to its high protein content. This study examined the effects of varying κ-carrageenan (CAR) and crayfish surimi (CSM) concentrations on the gelling properties of CAR-CSM composite gel and its intrinsic formation process. Our findings demonstrated that with the increasing concentration of carrageenan, the quality of CAR-CSM exhibited rising trend followed by subsequently fall. Based on the textural qualities, the highest quality CAR-CSM was achieved at 0.3% carrageenan addition. With the exception of chewiness, and the cooking loss of the gel system was 1.62%, whiteness was 82.35%, and the percentage of ß-sheets increased to 57.18%. Further increase in CAR (0.4-0.5%) addition resulted in internal build-up of LCAR-CSM, conversion of intermolecular forces into disulfide bonds and gel breakage. This study exudes timely recommendations for extending the CAR application for the continuous development of crayfish surimi and its derivatives and its overall economic worth.

Sugarcane bagasse biochar boosts maize growth and yield in salt-affected soil by improving soil enzymatic activities.

Salinization is a leading threat to soil degradation and sustainable crop production. The application of organic amendments could improve crop growth in saline soil. Thus, we assessed the impact of sugarcane bagasse (SB) and its biochar (SBB) on soil enzymatic activity and growth response of maize crop at three various percentages (0.5%, 1%, and 2% of soil) under three salinity levels (1.66, 4, and 8 dS m-1). Each treatment was replicated three times in a completely randomized block design with factorial settings. The results showed that SB and SBB can restore the impact of salinization, but the SBB at the 2% addition rate revealed promising results compared to SB. The 2% SBB significantly enhanced shoot length (23.4%, 26.1%, and 41.8%), root length (16.8%, 20.8%, and 39.0%), grain yield (17.6%, 25.1%, and 392.2%), relative water contents (11.2%, 13.1%, and 19.2%), protein (17.2%, 19.6%, and 34.9%), and carotenoid (16.3, 30.3, and 49.9%) under different salinity levels (1.66, 4, and 8 dS m-1, respectively). The 2% SBB substantially drop the Na+ in maize root (28.3%, 29.9%, and 22.4%) and shoot (36.1%, 37.2%, and 38.5%) at 1.66, 4, and 8 dS m-1. Moreover, 2% SBB is the best treatment to boost the urease by 110.1%, 71.7%, and 91.2%, alkaline phosphatase by 28.8%, 38.8%, and 57.6%, and acid phosphatase by 48.4%, 80.1%, and 68.2% than control treatment under 1.66, 4 and 8 dS m-1, respectively. Pearson analysis showed that all the growth and yield parameters were positively associated with the soil enzymatic activities and negatively correlated with electrolyte leakage and sodium. The structural equational model (SEM) showed that the different application percentage of amendments significantly influences the growth and physiological parameters at all salinity levels. SEM explained the 81%, 92%, and 95% changes in maize yield under 1.66, 4, and 8 dS m-1, respectively. So, it is concluded that the 2% SBB could be an efficient approach to enhance the maize yield by ameliorating the noxious effect of degraded saline soil.

The efficacy and safety of trastuzumab and albumin-bound paclitaxel with or without pyrotinib as neoadjuvant therapy for HER2-positive breast cancer: a prospective observational cohort study.

Background: In the past few years, the combination of trastuzumab and paclitaxel has become an important option for human epidermal growth factor receptor-2 (HER2)-positive breast cancer. Small molecule tyrosine kinase inhibitors (TKIs) can bring clinical benefit to HER2-positive breast cancer patients. However, the efficacy and safety of these two regimens have not been compared. This study explored the efficacy and safety of pyrotinib combined with trastuzumab and albumin-bound paclitaxel (nab-paclitaxel). Methods: Patients with newly diagnosed HER2-positive early or locally advanced breast cancer treated at The Tumor Hospital of Mudanjiang City from November 2020 to June 2022 were included. The control group received pertuzumab in combination with nab-paclitaxel, whereas the pyrotinib group received pyrotinib in combination with pertuzumab and nab-paclitaxel as treatment, in a 3-week cycle for 4 cycles. The primary endpoints of this study were total pathological complete response (tpCR) rate, breast pathological complete response (bpCR) rate, and the secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and the occurrence of adverse events (AEs). Results: A total of 72 patients were enrolled in the study and completed the study treatment. Baseline characteristics were well balanced between these two arms. In the control group, the tPCR rate was 23.68%, and the bpCR rate was 47.36%. In the pyrotinib group, the tPCR rate was 47.06%, and the bpCR rate was 64.71%. The tPCR rate in the pyrotinib group was significantly higher than that in the control group (P=0.049). The ORR in the pyrotinib group (67.65%) was significantly higher than that in the control group (42.11%, P=0.04 ). The median PFS (mPFS) for the control group was 9.24 months, with a mean PFS of 10.01±0.44 months [95% confidence interval (CI): 9.14-10.88 months]. In the pyrotinib group, mPFS was 9.74 months, with a mean PFS of 11.25±0.29 months (95% CI: 10.67-11.82 months). The PFS in the pyrotinib group was significantly longer than that in the control group (P=0.045). Safety results showed that the overall incidence of AEs in the control group was 68.42%, with a 3-grade adverse reaction rate of 21.05%. In the pyrotinib group, the overall incidence of AEs was 79.41%, with a 3-grade adverse reaction rate of 29.41%. The difference between the two groups was not statistically significant (P>0.05). Conclusions: Pyrotinib group in neoadjuvant treatment for HER2 positive breast cancer has obvious short-term efficacy advantages over control group. This treatment regimen can prolong PFS for 1 year, and the safety during medication is controllable. This study still has some limitations, with the relatively small sample size and relatively short follow-up period, and a further large-scale, multicenter, randomized controlled trial is necessary to verify the clinical value of this dual-target treatment regimen.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Statistical properties for an optically injected chaotic semiconductor laser with a high-pass filter.

Chaotic waveforms with Gaussian distributions are significant for laser-chaos-based applications such as random number generation. By exploring the injection parameter space of the optical injection semiconductor lasers, we numerically investigate the associated probability density functions of the generated chaotic waveforms when different high-pass filters with different cutoff frequencies are used. Our results demonstrate that the chaotic waveforms with Gaussian probability density functions can be obtained once the cutoff frequency of the high-pass filter is larger than the laser relaxation resonance frequency. Especially, we find that the Gaussian probability density function can reach a superhigh coefficient of determination R2 ≥ 99.5% and an ultralow skewness |S|<0.1 in a large parameter space by jointly controlling the injection parameter and cutoff frequency.

Status and influencing factors of nurses' burnout: A cross-sectional study during COVID-19 regular prevention and control in Jiangsu Province, China.

Background: Chinese nurses working with immense stress may have issues with burnout during COVID-19 regular prevention and control. There were a few studies investigating status of burnout and associated factors among Chinese nurses. However, the relationships remained unclear. Objectives: To investigate status and associated factors of nurses' burnout during COVID-19 regular prevention and control. Methods: 784 nurses completed questionnaires including demographics, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, Impact of Event Scale-revised, Perceived Social Support Scale, Connor-Davidson Resilience Scale, General Self-efficacy Scale and Maslach Burnout Inventory. Results: 310 (39.5%), 393 (50.1%) and 576 (73.5%) of respondents were at high risk of emotional exhaustion (EE), depersonalization (DP) and reduced personal accomplishment (PA). The risk of EE, DP and reduced PA were moderate, high and high. Nurses with intermediate and senior professional rank and title and worked >40 h every week had lower scores in EE. Those worked in low-risk department reported lower scores in PA. Anxiety, post-traumatic stress disorder (PTSD), self-efficacy and social support were influencing factors of EE and DP, while social support and resilience were associated factors of PA. Conclusion: Chinese nurses' burnout during COVID-19 regular prevention and control was serious. Professional rank and title, working unit, weekly working hours, anxiety, PTSD, self-efficacy, social support and resilience were associated factors of burnout.

Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson's disease.

BACKGROUND: Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. METHOD: We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed. RESULTS: Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8, p=0.0053; ArI-LHPRREM: r=-0.6, p=0.0373; SFI-LHPRNREM: r=-0.7, p=0.0204; SFI-LHPRREM: r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (ß=-0.24, p<0.001). CONCLUSION: Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. TRIAL REGISTRATION NUMBER: NCT02937727.

Efficient Deep Spiking Multilayer Perceptrons With Multiplication-Free Inference.

Advancements in adapting deep convolution architectures for spiking neural networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of multiplication-free inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in multilayer perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization (BN) to retain MFI compatibility and introduce a spiking patch encoding (SPE) layer to enhance local feature extraction capabilities. As a result, we establish an efficient multistage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pretraining or sophisticated SNN training techniques, our network secures a top-one accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-one accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.

A critical event frequent lead to reversible spinal cord injury during vertebral column resection surgery.

OBJECTIVE: To report a "critical phase" (between osteotomy completion and correction beginning) that will frequently lead to the reversible intraoperative neurophysiological monitoring (IOM) change during posterior vertebral column resection (PVCR) surgery. METHODS: The study sample consisted of 120 patients with severe spine deformity who underwent PVCR and deformity correction surgeries. Those patients were recruited consecutively from 2010 to 2018 January in our spine center. The detailed IOM data (the amplitude of MEP & SEP) and its corresponding surgical points were collected prospectively. Early and long-term postoperative neurologic outcomes were assessed for the following functions: motor, sensory, and pain at immediate postoperative and 1-year post-operation in this cases series. RESULTS: A total of 105 (105/120) patients presented varying degrees of IOM reduction in the critical phase; the mean IOM amplitude retention vs rescue rate was 27% ± 11.2 versus 58% ± 16.9, P < 0.01 (MEP) & 34% ± 8.3 versus 66% ± 12.4 P < 0.01 (SEP). Patients with postoperative spinal deficits often had a significantly longer IOM-alerting duration than the patients without (p < 0.01, Mann-Whitney U-test), and IOM-alerting duration greater than 39.5 min was identified as an independent predictor of the risk of postoperative spinal deficits. CONCLUSIONS: The reversible IOM events probably often appear in the critical phase during PVCR surgery. The new postoperative spinal deficits are possible for patients without satisfied IOM recovery or alerting duration greater than 39.5 min. Timely and suitable surgical interventions are useful for rescuing the IOM alerts.

Generalized sleep decoding with basal ganglia signals in multiple movement disorders.

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson's disease, Essential Tremor, Dystonia, Essential Tremor, Huntington's disease, and Tourette's syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors.

Background: Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors. Methods: We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors. Results: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively. Conclusion: Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.

OBJECTIVE: The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). METHODS: HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. RESULTS: Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. SIGNIFICANCE: The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. PLAIN LANGUAGE SUMMARY: In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.

Mental health and insomnia problems in healthcare workers after the COVID-19 pandemic: A multicenter cross-sectional study.

BACKGROUND: Healthcare workers (HCWs) are at increased risk of contracting coronavirus disease 2019 (COVID-19) as well as worsening mental health problems and insomnia. These problems can persist for a long period, even after the pandemic. However, less is known about this topic. AIM: To analyze mental health, insomnia problems, and their influencing factors in HCWs after the COVID-19 pandemic. METHODS: This multicenter cross-sectional, hospital-based study was conducted from June 1, 2023 to June 30, 2023, which was a half-year after the end of the COVID-19 emergency. Region-stratified population-based cluster sampling was applied at the provincial level for Chinese HCWs. Symptoms such as anxiety, depression, and insomnia were evaluated by the Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Insomnia Severity Index. Factors influencing the symptoms were identified by multivariable logistic regression. RESULTS: A total of 2000 participants were invited, for a response rate of 70.6%. A total of 1412 HCWs [618 (43.8%) doctors, 583 (41.3%) nurses and 211 (14.9%) nonfrontline], 254 (18.0%), 231 (16.4%), and 289 (20.5%) had symptoms of anxiety, depression, and insomnia, respectively; severe symptoms were found in 58 (4.1%), 49 (3.5%), and 111 (7.9%) of the participants. Nurses, female sex, and hospitalization for COVID-19 were risk factors for anxiety, depression, and insomnia symptoms; moreover, death from family or friends was a risk factor for insomnia symptoms. During the COVID-19 outbreak, most [1086 (76.9%)] of the participating HCWs received psychological interventions, while nearly all [994 (70.4%)] of them had received public psychological education. Only 102 (7.2%) of the HCWs received individual counseling from COVID-19. CONCLUSION: Although the mental health and sleep problems of HCWs were relieved after the COVID-19 pandemic, they still faced challenges and greater risks than did the general population. Identifying risk factors would help in providing targeted interventions. In addition, although a major proportion of HCWs have received public psychological education, individual interventions are still insufficient.

Whole exome sequencing identified a homozygous novel variant in DOP1A gene in the Pakistan family with neurodevelopmental disabilities: case report and literature review.

Background: Hereditary neurodevelopmental disorders (NDDs) are prevalent in poorly prognostic pediatric diseases, but the pathogenesis of NDDs is still unclear. Irregular myelination could be one of the possible causes of NDDs. Case presentation: Here, whole exome sequencing was carried out for a consanguineous Pakistani family with NDDs to identify disease-associated variants. The co-segregation of candidate variants in the family was validated using Sanger sequencing. The potential impact of the gene on NDDs has been supported by conservation analysis, protein prediction, and expression analysis. A novel homozygous variant DOP1A(NM_001385863.1):c.2561A>G was identified. It was concluded that the missense variant might affect the protein-protein binding sites of the critical MEC interaction region of DOP1A, and DOP1A-MON2 may cause stability deficits in Golgi-endosome protein traffic. Proteolipid protein (PLP) and myelin-associate glycoprotein (MAG) could be targets of the DOP1A-MON2 Golgi-endosome traffic complex, especially during the fetal stage and the early developmental stages. This further supports the perspective that disorganized myelinogenesis due to congenital DOP1A deficiency might cause neurodevelopmental disorders (NDDs). Conclusion: Our case study revealed the potential pathway of myelinogenesis-relevant NDDs and identified DOP1A as a potential NDDs-relevant gene in humans.

Platycodon grandiflorum saponins: Ionic liquid-ultrasound-assisted extraction, antioxidant, whitening, and antiaging activity.

This study explored the use of ionic liquid-ultrasound (ILU)-assisted extraction to enhance the extraction rate of Platycodon grandiflorum saponins (PGSs), and the content, extraction mechanism, antioxidant activity, whitening, and antiaging activity of PGSs prepared using ILU, ultrasound-water, thermal reflux-ethanol, and cellulase hydrolysis were compared. The ILU method particularly disrupted the cell wall, improved PGS extraction efficiency, and yielded a high total saponin content of 1.45 ± 0.02 mg/g. Five monomeric saponins were identified, with platycodin D being the most abundant at 1.357 mg/g. PGSs displayed excellent in vitro antioxidant activity and exhibited inhibitory effects on tyrosinase, elastase, and hyaluronidase. The results suggest that PGSs may have broad antioxidant, skin-whitening, and antiaging potential to a large extent. Overall, this study provided valuable insights into the extraction, identification, and bioactivities of PGSs, which could serve as a reference for future development and application of these compounds in the functional foods industry.

Identification of mine water source based on TPE-LightGBM.

Mine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl-, SO42- and HCO3- of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM's high accuracy and generalization ability have a good prospect for the identification of sudden water source types.

Blueberry anthocyanin based active intelligent wheat gluten protein films: Preparation, characterization, and applications for shrimp freshness monitoring.

The aim of this study was to prepare active intelligent gluten protein films using wheat gluten protein (WG) and apple pectin (AP) as film-forming matrices, and blueberry anthocyanin extract (BAE) as a natural indicator. SEM and FT-IR analyses demonstrated the successful immobilization of BAE in the film matrix by hydrogen bonding interactions and its compatibility with WG and AP. The resultant WG-AP/BAE indicator films demonstrated notable antioxidant activity, color stability, barrier qualities, pH and ammonia response sensitivity, and mechanical properties. Among them, WG-AP/BAE5 exhibited the best mechanical properties (TS: 0.83 MPa and EB: 242.23%) as well as the lowest WVP (3.92 × 10-8 g.m/m2.Pa.s), and displayed high sensitivity to volatile ammonia. In addition, WG-AP/BAE5 showed a color shift from purplish red to green to yellowish green, demonstrating the monitoring of shrimp freshness in real time. Consequently, this study offers a firm scientific foundation for the development of active intelligent gluten protein films and their use in food freshness assessments.