Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

Gas quenching and vacuum quenching process are widely applied to accelerate solvent volatilization to induce nucleation of perovskites in blade-coating method.In this work,we found these two pre-crystallization processes lead to different order of crystallization dynamics within the perovskite thin film,resulting in the differences of additive distribution.We then tailor-designed an additive molecule named 1,3-bis(4-methoxyphenyl)thiourea to obtain films with fewer defects and holes at the buried interface,and prepared perovskite solar cells with a certified efficiency of 23.75%.Furthermore,this work also demonstrates an efficiency of 20.18%for the large-area perovskite solar module(PSM)with an aperture area of 60.84 cm^(2).The PSM possesses remarkable continuous operation stability for maximum power point tracking of T_(90)>1000 h in ambient air.

Differential distribution of PINK1 and Parkin in the primate brain implies distinct roles

The vast majority of in vitro studies have demonstrated that PINK1 phosphorylates Parkin to work together in mitophagy to protect against neuronal degeneration.However,it remains largely unclear how PINK1 and Parkin are expressed in mammalian brains.This has been difficult to address because of the intrinsically low levels of PINK1 and undetectable levels of phosphorylated Parkin in small animals.Understanding this issue is critical for elucidating the in vivo roles of PINK1 and Parkin.Recently,we showed that the PINK1 kinase is selectively expressed as a truncated form(PINK1–55)in the primate brain.In the present study,we used multiple antibodies,including our recently developed monoclonal anti-PINK1,to validate the selective expression of PINK1 in the primate brain.We found that PINK1 was stably expressed in the monkey brain at postnatal and adulthood stages,which is consistent with the findings that depleting PINK1 can cause neuronal loss in developing and adult monkey brains.PINK1 was enriched in the membrane-bound fractionations,whereas Parkin was soluble with a distinguishable distribution.Immunofluorescent double staining experiments showed that PINK1 and Parkin did not colocalize under physiological conditions in cultured monkey astrocytes,though they did colocalize on mitochondria when the cells were exposed to mitochondrial stress.These findings suggest that PINK1 and Parkin may have distinct roles beyond their well-known function in mitophagy during mitochondrial damage.

Effects of aggregate size distribution and carbon nanotubes on the mechanical properties of cemented gangue backfill samples under true triaxial compression

The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious materials.Consequently,the true triaxial compression tests,CT scanning,SEM,and EDS tests were conducted on cemented gangue backfill samples(CGBSs)with various carbon nanotube concentrations(P_(CNT))that satisfied fractal theory for the PSD of aggregates.The mechanical properties,energy dissipations,and failure mechanisms of the CGBSs under true triaxial compression were systematically analyzed.The results indicate that appropriate carbon nanotubes(CNTs)effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging,and the optimal effect appears at P_(CNT)of 0.08wt%.Taking PSD fractal dimension(D)of 2.500 as an example,compared to that of CGBS without CNT,the peak strength(σ_(p)),axial peak strain(ε_(1,p)),elastic strain energy(Ue),and dissipated energy(U_(d))increased by 12.76%,29.60%,19.05%,and90.39%,respectively.However,excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration,manifesting a decrease inρ_(p),ε_(1,p),and the volumetric strain increment(Δε_(v))when P_(CNT)increases from 0.08wt%to 0.12wt%.Moreover,the addition of CNTs improved the integrity of CGBS after macroscopic failure,and crack extension in CGBSs appeared in two modes:detour and pass through the aggregates.Theσ_(p)and U_(d)firstly increase and then decrease with increasing D,and porosity shows the opposite trend.Theε_(1,p)andΔε_(v)are negatively correlated with D,and CGBS with D=2.150 has the maximum deformation parameters(ε_(1,p)=0.05079,Δε_(v)=0.01990)due to the frictional slip effect caused by coarse aggregates.With increasing D,the failure modes of CGBSs are sequentially manifested as oblique shear failure,"Y-shaped"shear failure,and conjugate shear failure.

Pretreatment red blood cell distribution width as a predictive marker for postoperative complications after laparoscopic pancreatoduodenectomy

BACKGROUND Red blood cell distribution width(RDW)is associated with the development and progression of various diseases.AIM To explore the association between pretreatment RDW and short-term outcomes after laparoscopic pancreatoduodenectomy(LPD).METHODS A total of 804 consecutive patients who underwent LPD at our hospital between March 2017 and November 2021 were retrospectively analyzed.Correlations between pretreatment RDW and clinicopathological characteristics and short-term outcomes were investigated.RESULTS Patients with higher pretreatment RDW were older,had higher Eastern Cooperative Oncology Group scores and were associated with poorer short-term outcomes than those with normal RDW.High pretreatment RDW was an independent risk factor for postoperative complications(POCs)(hazard ratio=2.973,95%confidence interval:2.032-4.350,P<0.001)and severe POCs of grade IIIa or higher(hazard ratio=3.138,95%confidence interval:2.042-4.824,P<0.001)based on the Clavien-Dino classification system.Subgroup analysis showed that high pretreatment RDW was an independent risk factor for Clavien-Dino classi-fication grade IIIb or higher POCs,a comprehensive complication index score≥26.2,severe postoperative pancreatic fistula,severe bile leakage and severe hemorrhage.High pretreatment RDW was positively associated with the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio and was negatively associated with albumin and the prognostic nutritional index.CONCLUSION Pretreatment RDW was a special parameter for patients who underwent LPD.It was associated with malnutrition,severe inflammatory status and poorer short-term outcomes.RDW could be a surrogate marker for nutritional and inflammatory status in identifying patients who were at high risk of developing POCs after LPD.

Novel Low-Carbon Optimal Operation Method for Flexible Distribution Network Based on Carbon Emission Flow

With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FDN),the calculation of carbon flow distribution in FDN is more difficult.To this end,this study constructs a model for low-carbon optimal operations within the FDN on the basis of enhanced carbon emission flow(CEF).First,the carbon emission characteristics of FDNs are scrutinized and an improved method for calculating carbon flow within these networks is proposed.Subsequently,a model for optimizing low-carbon operations within FDNs is formulated based on the refined CEF,which merges the specificities of DG and intelligent SOP.Finally,this model is scrutinized using an upgraded IEEE 33-node distribution system,a comparative analysis of the cases reveals that when DG and SOP are operated in a coordinated manner in the FDN,with the cost of electricity generation was reduced by 40.63 percent and the cost of carbon emissions by 10.18 percent.The findings indicate that the judicious optimization of areas exhibiting higher carbon flow rates can effectively enhance the economic efficiency of DN operations and curtail the carbon emissions of the overall network.

Numerical Simulation of Flow and Temperature Distribution in a Bottom-Blown Copper Bath

Smelting with oxygen bottom blowing is one of the main methods used in the frame of copper pyrometallurgy.With this approach,feed materials and oxygen-enriched air are introduced in reversed order to enhance multiphaseflow within the furnace.Understanding the flow structure and temperature distribution in this setup is crucial foroptimizing production.In this study,gas-liquid interactions,and temperature profiles under varying air-injectionconditions are examined by means of numerical simulation for a 3.2 m×20 m furnace.The results indicate that thehigh-velocity regions are essentially distributed near the lance within the reaction region and the flue gas outlet,while low-velocity regions are located close to the furnace walls on both side of the reaction region.Dead regionsappear in the sedimentation region,with gas velocities surpassing those of the molten phase.As the injection rateincreases from 0.50 to 0.80 Nm3/s,the stabilization time of the average liquid surface velocity decreases from 2.6 sto 1.9 s,exhibiting a similar trend to the gas holdup.During stabilization,the average liquid surface velocity risesfrom 0.505 to 0.702 m/s.The average turbulent kinetic energy(TKE)of the fluid in the molten bath increases from0.095 to 0.162 m^(2)/s^(2).The proportion of the area distribution with TKE greater than 0.10 m^(2)/s^(2) and the gas holdupat steady state both rise with an increase in the injection quantity.The maximum splashing height of the melt growsfrom approximately 0.756 to 1.154 m,with the affected area expanding from 14.239 to 20.498 m^(2).Under differentworking conditions with varying injection quantities,the average temperature changes in melt zone and flue gaszone of the furnace are small.The temperature in the melt and in the flue-gas zone spans the interval 1200℃–1257℃,and 1073℃–1121℃,respectively.The temperature distribution of the melt and flue gas reveals a patterncharacterized by elevated temperatures in the reaction zone,gradually transitioning to lower temperatures in thesedimentation region.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

A Case Study on Garbage Code Redistribution Methods for Heart Failure at City Level by Two Approaches

Cause of death surveillance data is most important for developing effective health policies,whose quality is crucially affected by the accuracy of the underlying cause of death(UCOD)provided in death certificates.The World Health Organization(WHO)has clearly defined a UCOD as“the disease or injury which initiated the train of morbid events leading directly to death,or the circumstance of the accident or violence which produced the fatal injuries”[1].

Red cell distribution width/platelet ratio estimates the 3-year risk of decompensation in Metabolic Dysfunction-Associated Steatotic Liver Disease-induced cirrhosis

BACKGROUND For compensated advanced chronic liver disease(cACLD)patients,the first decompensation represents a dramatically worsening prognostic event.Based on the first decompensation event(DE),the transition to decompensated advanced chronic liver disease(dACLD)can occur through two modalities referred to as acute decompensation(AD)and non-AD(NAD),respectively.Clinically Significant Portal Hypertension(CSPH)is considered the strongest predictor of decompensation in these patients.However,due to its invasiveness and costs,CSPH is almost never evaluated in clinical practice.Therefore,recognizing noninvasively predicting tools still have more appeal across healthcare systems.The red cell distribution width to platelet ratio(RPR)has been reported to be an indicator of hepatic fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease(MASLD).However,its predictive role for the decompensation has never been explored.AIM In this observational study,we investigated the clinical usage of RPR in predicting DEs in MASLD-related cACLD patients.METHODS Fourty controls and 150 MASLD-cACLD patients were consecutively enrolled and followed up(FUP)semiannually for 3 years.At baseline,biochemical,clinical,and Liver Stiffness Measurement(LSM),Child-Pugh(CP),Model for End-Stage Liver Disease(MELD),aspartate aminotransferase/platelet count ratio index(APRI),Fibrosis-4(FIB-4),Albumin-Bilirubin(ALBI),ALBI-FIB-4,and RPR were collected.During FUP,DEs(timing and modaities)were recorded.CSPH was assessed at the baseline and on DE occurrence according to the available Clinical Practice Guidelines.RESULTS Of 150 MASLD-related cACLD patients,43(28.6%)progressed to dACLD at a median time of 28.9 months(29 NAD and 14 AD).Baseline RPR values were significantly higher in cACLD in comparison to controls,as well as MELD,CP,APRI,FIB-4,ALBI,ALBI-FIB-4,and LSM in dACLD-progressing compared to cACLD individuals[all P<0.0001,except for FIB-4(P:0.007)and ALBI(P:0.011)].Receiving operator curve analysis revealed RPR>0.472 and>0.894 as the best cut-offs in the prediction respectively of 3-year first DE,as well as its superiority compared to the other non-invasive tools examined.RPR(P:0.02)and the presence of baseline-CSPH(P:0.04)were significantly and independently associated with the DE.Patients presenting baseline-CSPH and RPR>0.472 showed higher risk of decompensation(P:0.0023).CONCLUSION Altogether these findings suggest the RPR as a valid and potentially applicable non-invasive tool in the prediction of timing and modalities of decompensation in MASLD-related cACLD patients.

Size-dependent vibration and buckling of porous functionally graded microplates based on modified couple stress theory in thermal environments by considering a dual power-law distribution of scale effects

In this study,the thermodynamic behaviors of the intrinsic frequency and buckling temperature of rectangular plates of functionally graded materials(FGMs)are explored based on the modified couple stress theory(MCST)and the novel dual powerlaw scale distribution theory.The effects of linear,homogeneous,and non-homogeneous temperature fields on the frequency and buckling temperature of FGM microplates are evaluated in detail.The results show that the porosity greatly affects the mechanical properties of FGM plates,reducing their frequency and flexural temperature compared with non-porous plates.Different temperature profiles alter plate frequencies and buckling temperatures.The presence and pattern of scale effect parameters are also shown to be crucial for the mechanical response of FGM plates.The present research aims to provide precise guidelines for the micro-electro-mechanical system(MEMS)fabrication by elucidating the complex interplay between thermal,material,and structural factors that affect the performance of FGM plates in advanced applications.

Adaptive Update Distribution Estimation under Probability Byzantine Attack

The secure and normal operation of distributed networks is crucial for accurate parameter estimation.However,distributed networks are frequently susceptible to Byzantine attacks.Considering real-life scenarios,this paper investigates a probability Byzantine(PB)attack,utilizing a Bernoulli distribution to simulate the attack probability.Historically,additional detection mechanisms are used to mitigate such attacks,leading to increased energy consumption and burdens on distributed nodes,consequently diminishing operational efficiency.Differing from these approaches,an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks.In the proposed algorithm,a penalty strategy is initially incorporated during data updates to weaken the influence of the attack.Subsequently,an adaptive fusion weight is employed during data fusion to merge the estimations.Additionally,the reason why this penalty term weakens the attack has been analyzed,and the performance of the proposed algorithm is validated through simulation experiments.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

Integrated fire/flight control of armed helicopters based on C-BFGS and distributionally robust optimization

To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden.Considering the complex dynamic characteristics and the couplings of armed helicopters,an improved automatic attack system is con-structed to integrate the fire control system with the flight con-trol system into a unit.To obtain the optimal command signals,the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno(C-BFGS)algorithm combined with the trust region method.To address the uncertainties in the automatic attack system,the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties,and the dual solvable problem is analyzed by using the duality the-ory,conjugate function,and dual norm.Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accu-racy.

Simulation of Haloxylon Ammodendron Stand Basic Diameter Structure Based on Fuzzy Distribution Function

Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R2 values showed that Fuzzy-Γ1, Fuzzy-Γ2, Fuzzy-Γ3, Fuzzy-Γ4 had good fitting performance, among which Fuzzy-Γ1 had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ2 distribution function was the second, and Fuzzy-Γ4 distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ5 is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ1 and Fuzzy-Γ2.

Hexagon-Islands Density and Size Distribution on Growing Single-Walled Carbon Nanotubes in the Kinetic 5-Vertex Model

A kinetic 5-vertex model is used to investigate hexagon-islands formation on growing single-walled carbon nanotubes (SWCNT). In the model, carbon atoms adsorption and migration processes on the SWCNT edge are considered. These two dynamic processes are assumed to be mutually independent as well as mutually dependent as far as the whole growth of the nanotube is concerned. Key physical parameters of the model are the growth time t, the diffusion length Γ defined as the ratio of the diffusion rate D to the carbon atomic flux F and the SWCNT chiral angle. The kinetic equation that describes the nanotube edge dynamics is solved using kinetic Monte Carlo simulations with the Bortz, Kalos and Lebowitz update algorithm. The behaviors of islands density and size distribution are investigated within the growth parameters’ space. Our study revealed key mechanisms that enable the formation of a new ring of hexagons at the SWCNT edge. The growth occurs either by pre-existing steps propagation or by hexagon-islands growth and coalescence on terraces located between dislocation steps, depending on values of model parameters. This should offer a road map for edge design in nanotubes production. We also found that in appropriate growth conditions, the islands density follows Gaussian and generalized Wigner distributions whereas their size distribution at a given growth time shows a decreasing exponential trend.

Red cell distribution width-to-albumin ratio is a simple promising prognostic marker in acute cholangitis requiring biliary drainage

Background: It is crucial to assess the severity of acute cholangitis(AC). There are currently several prognostic markers. However, the accuracies of these markers are not satisfied. The present study aimed to investigate the predictive value of the red cell distribution width(RDW)-to-albumin ratio(RAR) for the prognosis of AC. Methods: We retrospectively evaluated consecutive patients diagnosed with AC between May 2019 and March 2022. RAR was calculated, and its predictive ability for in-hospital mortality, intensive care unit(ICU) admission, bacteremia, and the length of hospitalization were analyzed. Results: Out of 438 patients, 34(7.8%) died. Multivariate analysis showed that malignant etiology [odds ratio(OR) = 4.816, 95% confidence interval(CI): 1.936-11.980], creatinine(OR = 1.649, 95% CI: 1.095-2.484), and RAR(OR = 2.064, 95% CI: 1.494-2.851) were independent risk factors for mortality. When adjusted for relevant covariates, including age, sex, malignant etiology, Tokyo severity grading(TSG), Charlson comorbidity index, and creatinine, RAR significantly predicted mortality(adjusted OR = 1.833, 95% CI: 1.280-2.624). When the cut-off of RAR was set to 3.8, its sensitivity and specificity for mortality were 94.1% and 56.7%, respectively. Patients with an RAR of > 3.8 had a 20.9-fold(OR = 20.9, 95% CI: 4.9-88.6) greater risk of mortality than the remaining patients. The area under the curve value of RAR for mortality was 0.835(95% CI: 0.770-0.901), which was significantly higher than that of TSG and the other prognostic markers, such as C-reactive protein-to-albumin ratio, and procalcitonin-to-albumin ratio. Lastly, RAR was not inferior to TSG in predicting ICU admission, bacteremia, and the length of hospitalization. Conclusions: RAR successfully predicted the in-hospital mortality, ICU admission, bacteremia, and the length of hospitalization of patients with AC, especially in-hospital mortality. RAR is a promising marker that is more convenient than TSG and other prognostic markers for predicting the prognosis of patients with AC.

Spatial distribution of shallow landslides caused by Typhoon Lekima in 2019 in Zhejiang Province, China

In recent years, the coastal region of Southeast China has witnessed a significant increase in the frequency and intensity of extreme rainfall events associated with landfalling typhoons. The hilly and mountainous terrain of this area, combined with rapid rainfall accumulation, has led to a surge in flash floods and severe geological hazards. On August 10, 2019, Typhoon Lekima made landfall in Zhejiang Province, China, and its torrential rainfall triggered extensive landslides, resulting in substantial damage and economic losses. Utilizing high-resolution satellite images, we compiled a landslide inventory of the affected area, which comprises a total of 2,774 rainfallinduced landslides over an area of 2965 km2. The majority of these landslides were small to mediumsized and exhibited elongated, clustered patterns. Some landslides displayed characteristics of high-level initiation, obstructing or partially blocking rivers, leading to the formation of debris dams. We used the inventory to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to elevation, slope angle, faults, and road density. The landslides were predominantly located in hilly and low mountainous areas, with elevations ranging from 150 to 300 m, slopes of 20 to 30 degrees, and a NE-SE aspect. Notably, we observed the highest Landslide Number Density(LND) and Landslide Area Percentage(LAP) in the rhyolite region. Landslides were concentrated within approximately 4 km on either side of fault zones, with their size and frequency negatively correlated with distances to faults, roads, and river systems. Furthermore, under the influence of typhoons, regions with denser vegetation cover exhibited higher landslide density, reaching maximum values in shrubland areas. In areas experiencing significantly increased concentrated rainfall, landslide density also showed a corresponding rise. In terms of spatial distribution, the rainfall-triggered landslides primarily occurred in the northeastern part of the study area, particularly in regions characterized by complex topography such as Shanzao Village in Yantan Town, Xixia Township, and Shangzhang Township. The research findings offer crucial data on the rainfallinduced landslides triggered by Typhoon Lekima, shedding light on their spatial distribution patterns. These findings provide valuable references for mitigating risks and planning reconstruction in typhoon-affected area.

Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease

Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheimer’s disease affects the entire brain,further research is needed to elucidate alterations in mitochondrial metabolism in the brain as a whole.Here,we investigated the expression of several important mitochondrial biogenesis-related cytokines in multiple brain regions after treatment with neural stem cell-derived exosomes and used a combination of whole brain clearing,immunostaining,and lightsheet imaging to clarify their spatial distribution.Additionally,to clarify whether the sirtuin 1(SIRT1)-related pathway plays a regulatory role in neural stem cell-de rived exosomes interfering with mitochondrial functional changes,we generated a novel nervous system-SIRT1 conditional knoc kout AP P/PS1mouse model.Our findings demonstrate that neural stem cell-de rived exosomes significantly increase SIRT1 levels,enhance the production of mitochondrial biogenesis-related fa ctors,and inhibit astrocyte activation,but do not suppress amyloid-βproduction.Thus,neural stem cell-derived exosomes may be a useful therapeutic strategy for Alzheimer’s disease that activates the SIRT1-PGC1αsignaling pathway and increases NRF1 and COXIV synthesis to improve mitochondrial biogenesis.In addition,we showed that the spatial distribution of mitochondrial biogenesis-related factors is disrupted in Alzheimer’s disease,and that neural stem cell-derived exosome treatment can reverse this effect,indicating that neural stem cell-derived exosomes promote mitochondrial biogenesis.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.