Extraction of Dibenzyl Disulfide from Transformer Oils by Acidic Ionic Liquid.

In recent years, dibenzyl disulfide (DBDS) in transformer oils has caused many transformer failures around the world, and its removal has attracted more attention. In this work, nine imidazolium-based ionic liquids (ILs) were applied as effective, green desulfurization extractants for DBDS-containing transformer oil for the first time. The results show that the desulfurization ability of the ILs for DBDS followed the order of [BMIM]FeCl4 > [BMIM]N(CN)2 > [BMIM]SCN > [BMIM](C4H9O)2PO2 > [BMIM]MeSO4 > [BMIM]NTf2 > [BMIM]OTf > [BMIM]PF6 > [BMIM]BF4. Especially, [BMIM]FeCl4 ionic liquid had excellent removal efficiency for DBDS, with its S partition coefficient KN (S) being up to 2642, which was much higher than the other eight imidazolium-based ILs. Moreover, the extractive performance of [BMIM]FeCl4 increased with an increasing molar ratio of FeCl3 to [BMIM]Cl, which was attributed to its Lewis acidity and fluidity. [BMIM]FeCl4 ionic liquid could also avail in the desulfurization of diphenyl sulfide (DPS) from model oils. The experimental results demonstrate that π-π action, π-complexation, and Lewis acid-base interaction played important roles in the desulfurization process. Finally, the ([BMIM]FeCl4) ionic liquid could be recycled five times without a significant decrease in extractive ability.

Ecological niche shift and suitable area expansion of a globally invasive species Phthorimaea operculella.

Phthorimaea operculella is a major potato pest of global importance, early warning and detection of which are of significance. In this study, we analyzed the climate niche conservation of P. operculella during its invasion by comparing the overall climate niche from three dimensions, including the differences between native range (South America) and entire invaded region (excluding South America), the differences bwtween native range (South America) and five invaded continents (North America, Oceania, Asia, Africa, and Europe), as well as the differences between native region (South America) and an invaded region (China). We constructed ecological niche models for its native range (South America) and invaded region (China). The results showed that the climatic niche of the pest has expanded to varying degrees in different regions, indicating that the pest could well adapt to new environments during the invasion. Almost all areas of South America are suitable for P. operculella. In China, its suitable area is mainly concentrated in Shandong, Hebei, Tianjin, Beijing, Henan, Hubei, Yunnan, Guizhou, Sichuan, Hainan, northern Guangxi, southern Hunan, Anhui, Guangdong, Jiangsu, southern Shanxi, and southern Shaanxi. With increasing greenhouse gas emissions and global temperature, its suitable area will decrease at low latitude and increase gradually at high latitude. Specifically, the northern boundary will extend to Liaoning, Jilin, and the southeastern region of Inner Mongolia, while the western boundary extends to Sichuan and the southeast Qinghai-Tibet Plateau. The suitable area in the southeast Yunnan-Guizhou Plateau, Hainan Island, and the south of Yangtze River, will gradually decrease. The total suitable habitat area for P. operculella in China is projected to increase under future climate condition. From 2081 to 2100, under the three greenhouse gas emissions scenarios of ssp126, ssp370, and ssp585, the suitable area is expected to increase by 27.78, 165.54, and 140.41 hm2, respectively. Therefore, it is crucial to strengtehen vigilance and implement strict measures to prevent the further expansion of P. operculella.

Tandemly expanded OR17b in Himalaya ghost moth facilitates larval food allocation via olfactory reception of plant-derived tricosane.

Herbivorous insects utilize intricate olfactory mechanisms to locate food plants. The chemical communication of insect-plant in primitive lineage offers insights into evolutionary milestones of divergent olfactory modalities. Here, we focus on a system endemic to the Qinghai-Tibetan Plateau to unravel the chemical and molecular basis of food preference in ancestral Lepidoptera. We conducted volatile profiling, neural electrophysiology, and chemotaxis assays with a panel of host plant organs to identify attractants for Himalaya ghost moth Thitarodes xiaojinensis larvae, the primitive host of medicinal Ophiocordyceps sinensis fungus. Using a DREAM approach based on odorant induced transcriptomes and subsequent deorphanization tests, we elucidated the odorant receptors responsible for coding bioactive volatiles. Contrary to allocation signals in most plant-feeding insects, T. xiaojinensis larvae utilize tricosane from the bulbil as the main attractant for locating native host plant. We deorphanized a TxiaOR17b, an indispensable odorant receptor resulting from tandem duplication of OR17, for transducing olfactory signals in response to tricosane. The discovery of this ligand-receptor pair suggests a survival strategy based on food location via olfaction in ancestral Lepidoptera, which synchronizes both plant asexual reproduction and peak hatch periods of insect larvae.

Opsin mutants alter host plant selection by color vision in the nocturnal invasive pest Tuta absoluta.

In insects, vision is crucial in finding host plants, but its role in nocturnal insects is largely unknown. Vision involves responses to specific spectra of photon wavelengths and opsins plays an important role in this process. Long-wavelength sensitive opsin (LW opsin) and blue-sensitive opsin (BL opsin) are main visual opsin proteins and play important in behavior regulation.We used CRISPR/Cas9 technology to mutate the long-wavelength-sensitive and blue wavelength-sensitive genes and explored the role of vision in the nocturnal invasive pest Tuta absoluta. Light wave experiments revealed that LW2(-/-) and BL(-/-) mutants showed abnormal wavelength tropism. Both LW2 and BL mutations affected the preference of T. absoluta for the green environment. Mutations in LW2 and BL are necessary to inhibit visual attraction. The elimination of LW2 and BL affected the preference of leaf moths for green plants, and mutations in both induced a preference in moths for white plants. Behavioral changes resulting from LW2(-/-) and BL(-/-) mutants were not affected by sense of smell, further supporting the regulatory role of vision in insect behavior. To the best of our knowledge, this is the first study to reveal that vision, not smell, plays an important role in the host-seeking behavior of nocturnal insects at night, of which LW2 and BL opsins are key regulatory factors. These study findings will drive the development of the "vision-ecology" theory.

Enhancing mechanical properties of composite solid electrolyte by ultra-high molecular weight polymers.

Composite solid electrolytes combining the advantages of inorganic and polymer electrolytes are considered as one of the promising candidates for solid-state lithium metal batteries. Compared with ceramic-in-polymer electrolyte, polymer-in-ceramic electrolyte displays excellent mechanical strength to inhibit lithium dendrite. However, polymer-in-ceramic electrolyte faces the challenges of lack of flexibility and severely blocked Li+transport. In this study, we prepared polymer-in-ceramic film utilizing ultra-high molecular weight polymers and ceramic particles to combine flexibility and mechanical strength. Meanwhile, the ionic conductivity of polymer-in-ceramic electrolytes was improved by adding excess lithium salt in polymer matrix to form polymer-in-salt structure. The obtained film shows high stiffness (10.5 MPa), acceptable ionic conductivity (0.18 mS cm-1) and high flexibility. As a result, the corresponding lithium symmetric cell stably cycles over 800 h and the corresponding LiFePO4cell provides a discharge capacity of 147.7 mAh g-1at 0.1 C without obvious capacity decay after 145 cycles.

Synthesizing high performance LNMO cathode materials with porous structure by manipulating reynolds number in a microreactor.

The demand for Lithium-ion batteries (LIBs) has significantly grown in the last decade due to their extensive use electric vehicles. To further advance the commercialization of LIBs for various applications, there is a pressing need to develop electrode materials with enhanced performance. The porous microsphere morphology LiNixMn2-xO4(LNMO) is considered to be an effective material with both high energy density and excellent rate performance. Nevertheless, LNMO synthesis technology still has problem such as long reaction time, high energy consumption and environmental pollution. Herein, LNMO microsphere was successfully synthesized with short precursors reaction time (18 s) at 40 °C without using chelating agent by microreaction technology combined solid-state lithiation. The optimized LNMO cathode shows microsphere (∼8µm) morphology stacked by nano primary particles, with abundant mesoporous and fully exposed low-energy plane. The electrochemical analysis indicates that the optimized LNMO cathode demonstrates 97.33% capacity retention even after 200 cycles at 1C. Additionally, the material shows a highly satisfactory discharge capacity of 92.3 mAh·g-1at 10C. Overall, microreaction technology is anticipated to offer a novel approach in the synthesis of LNMO cathode materials with excellent performance.

Confidence sets for a level set in linear regression.

Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It has been realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, ie, the set of covariate values for which the regression function exceeds a predefined level, instead of the estimation of the regression function itself. The published work on estimation of the level set has thus far focused mainly on nonparametric regression, especially on point estimation. In this article, the construction of confidence sets for the level set of linear regression is considered. In particular, 1 - α $$ 1-\alpha $$ level upper, lower and two-sided confidence sets are constructed for the normal-error linear regression. It is shown that these confidence sets can be easily constructed from the corresponding 1 - α $$ 1-\alpha $$ level simultaneous confidence bands. It is also pointed out that the construction method is readily applicable to other parametric regression models where the mean response depends on a linear predictor through a monotonic link function, which include generalized linear models, linear mixed models and generalized linear mixed models. Therefore, the method proposed in this article is widely applicable. Simulation studies with both linear and generalized linear models are conducted to assess the method and real examples are used to illustrate the method.

timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination: A systematic review and meta-analysis.

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2 = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2 = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2 = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.

HDAC1: An Essential and Conserved Member of the Diverse Zn2+-Dependent HDAC Family Driven by Divergent Selection Pressure.

Zn2+-dependent histone deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histone proteins. These enzymes are essential in all living systems, playing key roles in cancer treatment and as potential pesticide targets. Previous phylogenetic analyses of HDAC in certain species have been published. However, their classification and evolutionary origins across biological kingdoms remain unclear, which limits our understanding of them. In this study, we collected the HDAC sequences from 1451 organisms and performed analyses. The HDACs are found to diverge into three classes and seven subclasses under divergent selection pressure. Most subclasses show species specificity, indicating that HDACs have evolved with high plasticity and diversification to adapt to different environmental conditions in different species. In contrast, HDAC1 and HDAC3, belonging to the oldest class, are conserved and crucial in major kingdoms of life, especially HDAC1. These findings lay the groundwork for the future application of HDACs.

Reduced lysosomal density in neuronal dendrites mediates deficits in synaptic plasticity in Huntington's disease.

Huntington's disease (HD) usually causes cognitive disorders, including learning difficulties, that emerge before motor symptoms. Mutations related to lysosomal trafficking are linked to the pathogenesis of neurological diseases, whereas the cellular mechanisms remain elusive. Here, we discover a reduction in the dendritic density of lysosomes in the hippocampus that correlates with deficits in synaptic plasticity and spatial learning in early CAG-140 HD model mice. We directly manipulate intraneuronal lysosomal positioning with light-induced CRY2:CIB1 dimerization and demonstrate that lysosomal abundance in dendrites positively modulates long-term potentiation of glutamatergic synapses onto the neuron. This modulation depends on lysosomal Ca2+ release, which further promotes endoplasmic reticulum (ER) entry into spines. Importantly, optogenetically restoring lysosomal density in dendrites rescues the synaptic plasticity deficit in hippocampal slices of CAG-140 mice. Our data reveal dendritic lysosomal density as a modulator of synaptic plasticity and suggest a role of lysosomal mispositioning in cognitive decline in HD.

Manipulating the crystal plane angle within the primary particle arrangement for the radial ordered structure in a Ni-rich cathode.

Ni-rich cathodes with a radial ordered microstructure have been proved to enhance materials' structural stability. However, the construction process of radial structures has not yet been clearly elaborated. Herein, the formation process of radial structures induced by different doped elements has been systematically investigated. The advanced Electron Back Scatter Diffraction (EBSD) characterization reveals that W-doped materials are more likely to form a low-angle arrangement between crystal planes of the primary particles and exhibit twin growth during sintering than a B-doped cathode. The corresponding High Angle Annular Dark Field-Scanning Transmission Electron Microscopy (HAADF-STEM) analysis further proves that the twin growth induced by W doping can promote the migration of Li+. Simultaneously, the W-doped sample reduces the (003) plane surface energy and promotes the retention of the crystal plane, which can effectively alleviate the structural degradation caused by Li+ (de)intercalation. At a cut-off voltage of 4.6 V, the W-doped cathode displays a capacity retention rate of 94.1% after 200 cycles at 1C. This work unveils the influence of different element doping on the structure from the perspective of crystal plane orientation within primary particles and points out the importance of the exposure and orientation of the crystal plane of the particles.

A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs.

We aimed to apply a potent deep learning network, NAFNet, to predict adverse pathology events and biochemical recurrence-free survival (bRFS) based on pre-treatment MRI imaging. 514 prostate cancer patients from six tertiary hospitals throughout China from 2017 and 2021 were included. A total of 367 patients from Fudan University Shanghai Cancer Center with whole-mount histopathology of radical prostatectomy specimens were assigned to the internal set, and cancer lesions were delineated with whole-mount pathology as the reference. The external test set included 147 patients with BCR data from five other institutes. The prediction model (NAFNet-classifier) and integrated nomogram (DL-nomogram) were constructed based on NAFNet. We then compared DL-nomogram with radiology score (PI-RADS), and clinical score (Cancer of the Prostate Risk Assessment score (CAPRA)). After training and validation in the internal set, ROC curves in the external test set showed that NAFNet-classifier alone outperformed ResNet50 in predicting adverse pathology. The DL-nomogram, including the NAFNet-classifier, clinical T stage and biopsy results, showed the highest AUC (0.915, 95% CI: 0.871-0.959) and accuracy (0.850) compared with the PI-RADS and CAPRA scores. Additionally, the DL-nomogram outperformed the CAPRA score with a higher C-index (0.732, P < 0.001) in predicting bRFS. Based on this newly-developed deep learning network, NAFNet, our DL-nomogram could accurately predict adverse pathology and poor prognosis, providing a potential AI tools in medical imaging risk stratification.

GO-CoNiP New Composite Material Modified Separator for Long Cycle Lithium-Sulfur Batteries.

Lithium-sulfur batteries with high capacity are considered the most promising candidates for next-generation energy storage systems. Mitigating the shuttle reaction and promoting catalytic conversion within the battery are major challenges in the development of high-performance lithium-sulfur batteries. To solve these problems, a novel composite material GO-CoNiP is synthesized in this study. The material has excellent conductivity and abundant active sites to adsorb polysulfides and improve reaction kinetics within the battery. The initial capacity of the GO-CoNiP separator battery at 1 C is 889.4 mAh g-1 , and the single-cycle decay is 0.063% after 1000 cycles. In the 4 C high-rate test, the single-cycle decay is only 0.068% after 400 cycles. The initial capacity is as high as 828.2 mAh g-1 under high sulfur loading (7.3 mg cm-2 ). In addition, high and low-temperature performance tests are performed on the GO-CoNiP separator battery. The first cycle discharge reaches 810.9 mAh g-1 at a low temperature of 0 °C, and the first cycle discharge reaches 1064.8 mAh g-1 at a high temperature of 60 °C, and both can run stably for 120 cycles. In addition, in situ Raman tests are conducted to explain the adsorption of polysulfides by GO-CoNiP from a deeper level.

A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study.

Background: Early preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is critical for further surgical treatment. However, insufficient accuracy in predicting LNM status for PTC based on ultrasound images is a problem that needs to be urgently resolved. This study aimed to clarify the role of convolutional neural networks (CNNs) in predicting LNM for PTC based on multimodality ultrasound. Methods: In this study, the data of 308 patients who were clinically diagnosed with PTC and had confirmed LNM status via postoperative pathology at Beijing Tiantan Hospital, Capital Medical University, from August 2018 to April 2022 were incorporated into CNN algorithm development and evaluation. Of these patients, 80% were randomly included into the training set and 20% into the test set. The ultrasound examination of cervical LNM was performed to assess possible metastasis. Residual network 50 (Resnet50) was employed for feature extraction from the B-mode and contrast-enhanced ultrasound (CEUS) images. For each case, all of features were extracted from B-mode ultrasound images and CEUS images separately, and the ultrasound examination data of cervical LNM information were concatenated together to produce a final multimodality LNM prediction. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the predictive model. Heatmaps were further developed for visualizing the attention region of the images of the best-working model. Results: Of the 308 patients with PTC included in the analysis, 158 (51.3%) were diagnosed as LNM and 150 (48.7%) as non-LNM. In the test set, when a triple-modality method (i.e., B-mode image, CEUS image, and ultrasound examination of cervical LNM) was used, accuracy was maximized at 80.65% (AUC =0.831; sensitivity =80.65%; specificity =82.26%), which showed an expected increased performance over B-mode alone (accuracy =69.00%; AUC =0.720; sensitivity =70.00%; specificity =73.00%) and a dual-modality method (B-mode image plus CEUS image: accuracy =75.81%; AUC =0.742; sensitivity =74.19%; specificity =77.42%). The heatmaps of our triple-modality model demonstrated a possible focus area and revealed the model's flaws. Conclusions: The PTC lymph node prediction model based on the triple-modality features significantly outperformed all the other feature configurations. This deep learning model mimics the workflow of a human expert and leverages multimodal data from patients with PTC, thus further supporting clinical decision-making.

Acupuncture Treatment in a Mouse Model of Chronic Hypoxia-Induced Cognitive Dysfunction.

The treatment of central nervous disorders has consistently posed significant challenges to the medical field. Acupuncture, a non-pharmacological practice rooted in traditional Chinese medicine, entails the insertion of fine needles into precise points on the body and is commonly employed for the management of diverse conditions. Recently, acupuncture has emerged as a promising therapeutic intervention for a range of neurological diseases, including anxiety and respiratory disorders. However, the potential of acupuncture in treating cognitive dysfunction induced by chronic hypoxia has not yet been explored. This paper presents a comprehensive protocol for establishing a mouse model of chronic hypoxia-induced cognitive impairment, administering mild anesthesia, performing acupuncture treatment, and assessing behavioral changes and memory abilities using open field tests and water mazes. The step-by-step protocol provides detailed instructions on accurately locating and positioning acupoints and needles for cognitive improvement. By employing this protocol, researchers can conduct systematic studies to thoroughly evaluate the therapeutic potential of acupuncture for cognitive dysfunction.

Regeneration of Spent Lithium Manganate Batteries into Al-Doped MnO2 Cathodes toward Aqueous Zn Batteries.

Large quantities of spent lithium-ion batteries (LIBs) will inevitably be generated in the near future because of their wide application in many fields. It will cause not only resource waste but also environmental pollution if these spent batteries are not properly handled. Until now, the recycling of spent lithium manganate batteries has centered on high-valuable elements such as lithium; however, manganese element and current collector Al foil have not yet attracted wide attention. In this work, aluminum-doped manganese dioxide was synthesized by overall recycling cathode active materials and current collector Al foil from a spent lithium manganate battery. Employing such aluminum-doped manganese dioxide as the cathode material of aqueous Zn batteries, it displays better electrochemical performance than manganese dioxide prepared by only recycling the cathode active materials. The overall recycling not only simplifies the recycling process but also realizes high-value recycling of spent lithium manganate batteries. We offer new tactics for overall recycling of cathodes from spent LIBs and designing high-performance manganese dioxide cathodes for aqueous Zn batteries.

[Efficacy of intramuscular injection of Xiyanping injection for the treatment of acute bronchitis in children: a multicenter, randomized, parallel-controlled clinical study]. / å–œç‚Žå¹³æ³¨å°„æ¶²è‚Œå† æ³¨å°„æ²»ç–—å°å„¿æ€¥æ€§æ”¯æ°”ç®¡ç‚Žçš„ç–—æ•ˆï¼šä¸€é¡¹å¤šä¸­å¿ƒã€éšæœºã€å¹³è¡Œå¯¹ç §ä¸´åºŠç ”ç©¶.

OBJECTIVES: To study the efficacy and safety of Xiyanping injection through intramuscular injection for the treatment of acute bronchitis in children. METHODS: A prospective study was conducted from December 2021 to October 2022, including 78 children with acute bronchitis from three hospitals using a multicenter, randomized, parallel-controlled design. The participants were divided into a test group (conventional treatment plus Xiyanping injection; n=36) and a control group (conventional treatment alone; n=37) in a 1:1 ratio. Xiyanping injection was administered at a dose of 0.3 mL/(kg·d) (total daily dose ≤8 mL), twice daily via intramuscular injection, with a treatment duration of ≤4 days and a follow-up period of 7 days. The treatment efficacy and safety were compared between the two groups. RESULTS: The total effective rate on the 3rd day after treatment in the test group was significantly higher than that in the control group (P<0.05), while there was no significant difference in the total effective rate on the 5th day between the two groups (P>0.05). The rates of fever relief, cough relief, and lung rale relief in the test group on the 3rd day after treatment were higher than those in the control group (P<0.05). The cough relief rate on the 5th day after treatment in the test group was higher than that in the control group (P<0.05), while there was no significant difference in the fever relief rate and lung rale relief rate between the two groups (P>0.05). The cough relief time, daily cough relief time, and nocturnal cough relief time in the test group were significantly shorter than those in the control group (P<0.05), while there were no significant differences in the fever duration and lung rale relief time between the two groups (P>0.05). There was no significant difference in the incidence of adverse events between the two groups (P>0.05). CONCLUSIONS: The overall efficacy of combined routine treatment with intramuscular injection of Xiyanping injection in the treatment of acute bronchitis in children is superior to that of routine treatment alone, without an increase in the incidence of adverse reactions.

Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty.

Due to the impact of COVID-19, a significant influx of emergency patients inundated the intensive care unit (ICU), and as a result, the treatment of elective patients was postponed or even cancelled. This paper studies ICU bed allocation for three categories of patients (emergency, elective, and current ICU patients). A two-stage model and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to obtain ICU bed allocation. In the first stage, bed allocation is examined under uncertainties regarding the number of emergency patients and their length of stay (LOS). In the second stage, in addition to including the emergency patients with uncertainties in the first stage, it also considers uncertainty in the LOS of elective and current ICU patients. The two-stage model aims to minimize the number of required ICU beds and maximize resource utilization while ensuring the admission of the maximum number of patients. To evaluate the effectiveness of the model and algorithm, the improved NSGA-II was compared with two other methods: multi-objective simulated annealing (MOSA) and multi-objective Tabu search (MOTS). Drawing on data from real cases at a hospital in Lyon, France, the NSGA-II, while catering to patient requirements, saves 9.8% and 5.1% of ICU beds compared to MOSA and MOTS. In five different scenarios, comparing these two algorithms, NSGA-II achieved average improvements of 0%, 49%, 11.4%, 9.5%, and 17.1% across the five objectives.