Weight Balance Research Articles

Leptin and risk factors for atherosclerosis: A review.

Leptin is a hormone secreted primarily by adipose tissue. It regulates an organism's metabolism, energy balance, and body weight through a negative feedback mechanism. When a person or animal has low body fat and little energy, the leptin level in the body decreases, and conversely, when there is an excess of nutrients, the leptin level increases, giving a feeling of satiety. However, when leptin levels are abnormal (too high or too low) for a number of reasons, it can negatively affect your health, inducing inflammatory responses, obesity, and other problems. Many studies have shown that abnormal leptin levels, such as hyperleptinemia, are closely associated with common risk factors for atherosclerosis (AS). This review systematically states the relationship between leptin and common risk factors for AS (inflammation, obesity, diabetes mellitus, hypertension, and sleep disorders) and provides some new thoughts on the future direction of research on both. Because the abnormal level of leptin will have adverse effects on multiple atherosclerotic risk factors, how to regulate the leptin level of patients with AS, and whether we can treat and prevent AS by intervening the leptin level, these may be our new research directions in the future.

Estimating drug concentration-response relationships by applying causal inference methods for continuous point exposures and time-to-event outcomes.

In clinical development, it is useful to characterize the causal relationship between individual drug concentrations and clinical outcomes in large phase III trials of new therapeutic agents because it can provide insights on whether increasing the currently administered drug dose may lead to better outcomes. However, estimating causal effects of drug concentration is complicated by the fact that drug concentration is a continuous measure and it is usually influenced by patient-level prognostic characteristics such as body weight and sex. In this article, we compare two approaches to estimate causal effects of continuous point exposures on time-to-event outcomes: (a) outcome regression (OR) and (b) weighting. In particular, we make the first direct comparison of the balancing weights, inverse probability weighting and OR methods for estimating the effects of continuous exposures on time-to-event outcomes in simulations and demonstrate that these methods can exhibit markedly different behaviours that subsequently lead to a change in the conclusions. To improve weighted exposure effect estimators, we also propose a new simple-to-apply diagnostic to detect when such estimators might be subject to severe bias, and demonstrate its effectiveness in simulations. Finally, we apply these methods to an example of multiple sclerosis drug development by providing causal effect estimates of average ocrelizumab concentrations on time-to-event disability progression outcomes.

The effect of gunshot detection technology on evidence collection and case clearance in Kansas City, Missouri

Abstract Objectives This study tests whether (1) shots fired calls for service in the gunshot detection technology (GDT) target area are more likely to be classified as unfounded; (2) police responses to shootings in the GDT target area are more likely to recover ballistic evidence or firearms; and (3) shootings in the GDT target area are more likely to be cleared. Methods Entropy balancing created a weighted control group that equaled the treatment group across a range of covariates. GDT effect was tested through logistic regression models with entropy balancing weights set as probability weights. Results Shots fired occurring in the GDT target area were 15% more likely to be classified as unfounded compared to control cases. GDT did not significantly influence the likelihood of evidence collection or case clearance in shooting incidents. Conclusions GDT may not add investigative value to police responses to shooting incidents and may increase patrol workload.

Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks

To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.

Shikimic acid recovers diarrhea and its complications in SD rats fed lactose diet to induce diarrhea

BackgroundDiarrhea is the increase of excretion of human water content and an imbalance in the physiologic processes of the small and large intestine while shikimic acid is an important biochemical metabolite in plants. This study aims to study the anti-diarrheal activity of shikimic acid through restoring kidney function, antioxidant activity, inflammatory markers, sodium/potassium-ATPase activity, apoptosis genes, and histology of the kidney in SD rats fed lactose diet to induce diarrhea.ResultsThirty-six male SD rats (150 ± 10 g, 12 weeks old) were divided into 2 equal groups (18 rats/group) as follows: normal and diarrheal rats. Normal rats were divided into 3 equal groups of 6 rats each: the control, shikimic acid, and desmopressin drug groups. Diarrheal rats were also divided into 3 equal groups of 6 rats each: diarrheal, diarrheal rats + shikimic acid, and diarrheal rats + desmopressin drug groups. Shikimic acid restored serum urea and creatinine, urinary volume, kidney weight, sodium, potassium, and chloride balance in serum and urine. The acid returned the antioxidant (superoxide dismutase, glutathione peroxidase, catalase, malondialdehyde, NADPH oxidase activity, conjugated dienes, and oxidative index) activity and the inflammatory markers (tumor necrosis factor-α, interleukin-1β, interleukin-6, and interleukin-10) to values approaching the control values. Shikimic acid also restored the sodium/potassium-ATPase activity, the apoptosis genes p53 and bcl-2, and the histology of kidney tissue in diarrheal rats to be near the control group.ConclusionsShikimic acid rescues diarrhea and its complications through restoring kidney function, serum and urinary electrolytes, antioxidant activity, inflammatory markers, sodium/potassium-ATPase activity, the apoptosis genes, and the histology of the kidney in diarrheal rats to approach the control one.

Abstract 14169: Deletion of P21 Activated Kinase-1 Modulates Energy Homeostasis and Affects Cardiac Function

Introduction: We investigated the consequences of P21-activated kinase-1 (PAK1) deletion on cardiac function, body weight, body composition, and energy balance using a mouse model of global PAK1 knock out. PAK1 is a protein kinase that plays a role in multiple aspects of cardiac health. We hypothesize that PAK1 plays a role in cardiometabolic health including cardiac function and adipose tissue homeostasis. Methods: We used PAK1 -/- in the FVB/N background. We included 10-12-week-old male and female mice. Age and sex-matched FVB/N mice were used as wild-type (WT) controls. Most results included both male and female mice together, as no differences were found between sexes. Ca 2+ kinetics was measured in neonatal cardiomyocytes. Results: Deletion of PAK1 was found to have a significant impact on cardiac function. Specifically, slower Ca 2+ release and reuptake kinetics accompanied with decreased phospholamban phosphorylation was observed in PAK1 -/- mice. These changes affected cardiac systolic (EF, s’) and lusitropy parameters (E/e’, IVRT). Moreover, our findings revealed body weight differences between PAK1 -/- and WT at 12 weeks, accompanied with increased % fat mass and decreased % lean mass in PAK1 -/- , indicating a shift towards adiposity. Further analysis revealed that PAK1 -/- mice had increased weights of white adipose tissue (WAT), and brown adipose tissue (BAT). Moreover, we found increased food intake, increased number of feeding bouts and decreased energy expenditure accompanied with decreased thermogenic markers in subcutaneous WAT and BAT in PAK1 -/- , further contributing to the obesogenic phenotype (see table for results). Conclusions: Deletion of PAK1 affected systolic and diastolic functions, and resulted in increased energy intake, decreased energy expenditure, and increased adiposity. Therefore, targeting PAK1 signaling pathways may represent a promising therapeutic strategy for improving cardiometabolic diseases.

Research on Dueling Double Deep Q Network Algorithm Based on Single-Step Momentum Update

Vehicle behavior decision control plays a crucial role in the development of autonomous driving. However, existing autonomous driving behavior decision control algorithms based on deep reinforcement learning face several challenges, such as low efficiency in updating target network data and a lack of effective balancing between old and new experiences. To address these issues, this paper proposes a dueling double deep Q network (dueling DDQN) algorithm based on a single-step momentum update mechanism. Firstly, a single-step momentum update mechanism is designed to significantly improve the update speed of target network parameters and achieve a balanced weighting of old and new experiences during the parameter update process. Subsequently, the network structures of dueling networks and DDQNs are integrated to enhance the understanding capability of autonomous vehicles concerning their current states. Finally, tests are conducted on the OpenAI Gym simulation platform to validate the effectiveness of the proposed algorithm. The results verified that the dueling DDQN algorithm with single-step momentum updates contributes to improving the convergence speed of autonomous driving car behavior decisions. Compared with the DQN and DDQN algorithms, the proposed algorithm achieved a success rate increase of 6.0 and 8.4 percentage points in the challenging three-lane highway Test Scenario 1, and a success rate increase of 16.7 and 2.9 percentage points in Test Scenario 2, respectively. These findings demonstrate a safer and more efficient performance in autonomous driving decision-making.

Sensitivity Study of Surface Roughness Process Parameters in Belt Grinding Titanium Alloys

In order to obtain the optimum range of process parameters for abrasive belt grinding of titanium alloys to achieve a surface roughness within a given range, titanium alloy TC4 was selected as the research object, and experiments on abrasive belt grinding surface roughness were conducted. Firstly, an empirical formula for the surface roughness of titanium alloys after abrasive belt grinding was constructed based on the balanced weight analysis of the process parameters for titanium alloy surface roughness. Sensitivity analysis was carried out to identify the process parameters with the greatest effect on surface roughness, and the stable and unstable domains of the process parameters were determined. Combined with range analysis in orthogonal experiments, the influence curves of the process parameters on surface roughness were obtained, and the optimal parameter ranges were selected. The research results showed that surface roughness is the most sensitive to changes in abrasive grain size and the least sensitive to changes in abrasive belt linear speed. The optimal ranges of abrasive grain size, abrasive belt linear speed, and grinding pressure were determined to be 120# to 150#, 15 m/s to 20 m/s, and 10 N to 15 N, respectively. This study provides a theoretical method and experimental basis for the control of surface roughness in abrasive belt grinding of titanium alloys.

Solvent-free and biodegradable pressure-sensitive adhesive based on chlorosulfonated Poly(propylene carbonate)

The challenge of pressure-sensitive adhesives (PSAs) is to develop solvent-free and biodegradable PSAs. By chlorosulfonated reaction of poly(propylene carbonate)s (PPC) and sulfuryl chloride, a series of biodegradable and solvent-free chlorosulfonated poly(propylene carbonate)s (CSPPCs) with different molecular weight have been acquired. Different from the traditional fabrication of PSAs, introducing polar groups (sulfur and chlorine atoms) onto PPC is combined with the degradation of PPC. For CSPPCs, the elasticity is related to the molecular weight, and the viscosity is related with the polar groups contents, resulting that the balance of the molecular weight and the polar groups contents is significant to adhesion properties. Among CSPPCs, the balance of molecular weight and the polar group contents is most appropriate and the peel strength is highest, when the number-average molecular weight is equal to 15.5 kg/mol. In addition, peel strength can be further improved by chain extension or crosslinking of CSPPCs. From the point of view of environmental protection, the fabrication of CSPPCs is a new and economical method of producing biodegradable and solvent-free PSAs to meet the ever-increasing demands of society.

Impact of leptin deficiency on male tibia and vertebral body 3D bone architecture independent of changes in body weight.

Leptin an adipokine with potent effects on energy balance and body weight plays an important role in defining bone architecture in growing mammals. However, major changes in body weight can also influence morphology of trabecular and cortical bone. Therefore, we examined the impact of leptin deficiency on tibia and vertebral body 3D bone architecture independent of changes in body weight. Furthermore, advances in computational 3D image analysis suggest that average morphological values may mask regional specific differences in trabecular bone thickness. The study utilized leptin-deficient Ob/Ob mice (n = 8) weight-paired to C57BL/6 (C57) control mice (n = 8) which were split into either lean or obese groups for 24 ± 2 weeks. Whole tibias and L3 vertebrae were fixed before high resolution microcomputed tomography (μCT) scanning was performed. Leptin deficiency independent of body weight reduced tibia cortical bone volume, trabecular bone volume/tissue volume, number, and mineral density. Mean tibia trabecular thickness showed no significant differences between all groups; however, significant changes in trabecular thickness were found when analyzed by region. This study demonstrates that leptin deficiency significantly impacts tibia and vertebral body trabecular and cortical bone 3D architecture independent of changes in body weight.

Ankle stiffness asymmetry is associated with balance function in individuals with chronic stroke

Ankle joint is one of important contributors on balance in stroke survivors. This study aimed to investigate the relationships of ankle stiffness symmetry ratios along the talocrural and subtalar axes with clinical balance measures and weight distribution during quiet standing in ambulatory chronic post-stroke survivors. The clinical trials involved 15 ambulatory elderly with chronic post-stroke hemiparesis and 15 healthy controls. Ankle stiffness was evaluated during non-weight-bearing isokinetic passive biaxial ankle movements, and ankle stiffness symmetry ratios between paretic and non-paretic ankle stiffness (SR: Inversion/Eversion SRIE & Dorsi-/Plantarflexion SRDP) were measured. A certified physiotherapist evaluated the Berg Balance Scale (BBS) and weight-distribution ratio (WDR) on bilateral force plates during quiet standing. Correlation coefficients, the factor analysis, and Pearson linear multiple regression were assessed with measured parameters. Correlation coefficients showed significances in-betweens; BBS and SRDP (r = −0.543, p = 0.022), WDR and SRIE (r = −0.667, p = 0.004), SRIE and SRDP (r = −0.604, p = 0.011). The exploratory factor analysis suggested four extracted factors; (1) Balance & Gait, (2) Stroke, (3) Symmetry and (4) Dimension. The first and second factors include general and pathological characteristics in stoke participants respectively. The third factor is associated with symmetrical characteristics explaining up to 99.9% of the variance. Multiple regression analysis showed ankle stiffness ratios predict BBS up to 60% of variance. The biaxial ankle stiffness ratio is a useful clinical variable that assesses balance function, in ambulatory chronic stroke survivors.

Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals

Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify different mental disorders in the following ecological context accurately: (1) using raw EEG data, (2) collected during rest, (3) during both eye open, and eye closed conditions, (4) at short 2-min duration, (5) on participants with different psychiatric conditions, (6) with some overlapping symptoms, and (7) with strongly imbalanced classes. To tackle this challenge, we designed and optimized a transformer-based architecture, where class imbalance is addressed through focal loss and class weight balancing. Using the recently released TDBRAIN dataset (n= 1274 participants), our method classifies each participant as either a neurotypical or suffering from major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints (SMC), or obsessive–compulsive disorder (OCD). We evaluate the performance of the proposed architecture on both the window-level and the patient-level. The classification of the 2-min raw EEG data into five classes achieved a window-level accuracy of 63.2% and 65.8% for open and closed eye conditions, respectively. When the classification is limited to three main classes (MDD, ADHD, SMC), window level accuracy improved to 75.1% and 69.9% for eye open and eye closed conditions, respectively. Our work paves the way for developing novel AI-based methods for accurately diagnosing mental disorders using raw resting-state EEG data.

Chronic consumption of avocado seed (Persea americana) promotes a negative energy balance and body weight reduction in high-fat diet exposed mice: Implications for functional foods

Obesity is the main challenge for public health systems. Functional foods have emerged as an alternative to the ineffectiveness of pharmacological therapies. However, it is necessary to investigate to find sources of raw material for its preparation. The aim of this work was to evaluate the functional effects of residual avocado seed in diet-induced obese mice. Animals were fed a high-fat diet supplemented with avocado seed (1.25%) for 6 weeks. Body weight, food intake, satiety, physical activity, and energy expenditure-involved gene expression were measured. Supplementation with avocado seed led to reduced body weight, delayed satiety, increased physical activity, and induced Dio2 and Adrb1 expression in hypothalamus and white adipose tissue, respectively. No changes in markers of hepatic injury were observed. In conclusion, avocado seed exerts functional effects on obese mice, by promoting energy expenditure, without toxicity effects, making it possible to use this by-product in the development of functional foods.

The effect of adding fermented shrimp waste extract in the ration on the balance of protein efficiency in egg production

Background: The purpose of the study was to determine and obtain the level of addition of fermented shrimp waste extract in the ration as a feed additive that produces an optimal protein efficiency balance value in the egg production of purebred chickens. Materials and Methods: The study used 40 40-week-old layer laying hens in 20 cage units. The study used a randomized design method complete with five types of treatment, consisting of R 0 (ration without the use of fermented shrimp waste extract), R1 (ration containing 0.5% fermented shrimp waste extract), R2 (ration containing 1.0% fermented shrimp waste extract), R3 (ration containing 1.5% fermented shrimp waste extract), and R4 (ration containing 2.0% fermented shrimp waste extract). Each treatment was repeated four times with the observed variables being ration consumption, protein consumption, egg weight, and protein efficiency balance. The difference in effect between treatments was tested using the Tukey Test. Results: The results of the study found that the addition of fermented shrimp waste extract in the ration affected ration consumption and protein consumption, but did not affect egg weight and protein efficiency balance. Conclusions: The use of fermented shrimp waste extract in the 2% ration is optimal for the balance value of protein efficiency.

PIEZO1 promotes ATP release from periodontal ligament cells following compression force.

Orthodontic mechanical force on the periodontal ligament induces extracellular adenosine triphosphate (ATP) release. However, mechanosensitive molecules have not been confirmed functionally in periodontal ligament cells. In the present study, we examined the roles of mechanosensitive PIEZO channels in the mechanically stimulated release of ATP in human periodontal ligament fibroblasts (HPdLFs). To examine PIEZO expression in HPdLFs, we performed reverse transcription-quantitative polymerase chain reaction, fluorescent immunostaining, and Ca2+ imaging. ATP concentrations were measured in culture medium after applications of the PIEZO1 agonist Yoda1 and compression force in a newly developed in vitro weight-loaded cell model (IVWLC) using balance weights and a 48-well plate. The mechanosensitive channel inhibitor GsMTx4 and the ATP-releasing route inhibitors clodronic acid, meclofenamic acid, and probenecid were used. To suppress PIEZO1 expression, short interference RNA (siRNA) treatment of the PIEZO1 gene was performed. PIEZO1 mRNA was expressed more abundantly than PIEZO2 mRNA in HPdLFs. HPdLF cell bodies were immunoreactive to anti-PIEZO1 antibody. Yoda1 increased intracellular Ca2+ and extracellular ATP concentrations in a dose-dependent manner. ATP release was inhibited by GsMTx4 and inhibitors of ATP release routes. In the IVWLC, HPdLFs released ATP in response to compression force but not in response to hypoxic stimulation that was simultaneously applied to cells. Mechanically stimulated ATP release was inhibited by GsMTx4, inhibitors of ATP-releasing routes and siRNA treatment of PIEZO1. PIEZO1 on the cell membranes of HPdLFs is activated by compression force and then induces ATP release via intracellular Ca2+-dependent exocytosis and ATP-permeable channels.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

A Pilot Study of Idiopathic Toe Walking: Measures and Outcomes

This pilot study sought to determine the feasibility of quantifying sensory processing, postural control, motor skill development and participation in daily life in children with idiopathic toe walking (ITW) compared to children without ITW. A nonrandomized, prospective case-control design was employed. Twenty participants were recruited. Computerized weight bearing and postural control data was successfully collected. Differences were found in forefoot/rearfoot weight bearing, balance, sensory-motor abilities and parent reported participation challenges. Sensory processing results were inconclusive. Findings can inform standardized assessment and treatment of ITW.

A comprehensive study of the effects of various design variables on the dynamic behavior and vibration characteristics of a front-loading washing machine

This paper presents the findings of a comprehensive study of the effects of various design variables on the dynamic behavior and vibration characteristics of a front-loading washing machine. A multibody dynamics model of the washing machine with a set of 9 key design variables is developed using the commercial multibody simulation software Adams View. The design variables are implemented to enable automated modifications of the dynamics model for certain parameters such as the geometric orientations of the springs and dampers in the washing machine. This dynamics model is employed to perform a series of numerical simulations by considering a range of parametric values for the design variables. Numerical convergence analysis is performed to ensure that the simulation solutions are converged. Using the simulation solutions, the time histories of displacements at select points of the washing machine during operation (from transient till steady-state motion) are analyzed to understand how the natural frequencies of the washing machine are affected by various design variables. Finally, a sensitivity analysis is carried out to determine the design variables that have the strongest impact on the dynamic behavior of the washing machine. Of all the design variables affecting the dynamic behavior of the washer model, the weight balancer mass density and the damping coefficient were found to have the strongest effect.

Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data

Abstract With balanced training sample (TS) data, learning algorithms offer good results in lithology classification. Meanwhile, unprecedented lithological mapping in remote places is predicted to be difficult, resulting in limited and unbalanced samples. To address this issue, we can use a variety of techniques, including ensemble learning (such as random forest [RF]), over/undersampling, class weight tuning, and hybrid approaches. This work investigates and analyses many strategies for dealing with imbalanced data in lithological classification based on RF algorithms with limited drill log samples using remote sensing and airborne geophysical data. The research was carried out at Komopa, Paniai District, Papua Province, Indonesia. The class weight tuning, oversampling, and balance class weight procedures were used, with TSs ranging from 25 to 500. The oversampling approach outperformed the class weight tuning and balance class weight procedures in general, with the following metric values: 0.70–0.80 (testing accuracy), 0.43–0.56 (F1 score), and 0.32–0.59 (Kappa score). The visual comparison also revealed that the oversampling strategy gave the most reliable classifications: if the imbalance ratio is proportionate to the coverage area in each lithology class, the classifier capability is optimal.

Perioperative continuous body weight measurements with load cells under the bed legs in patients undergoing abdominal surgery

BACKGROUND Both excessive positive fluid balance and body weight increase after surgery are risk factors for poor postoperative outcomes. The use in clinical practice and the value of perioperative body weight measurements are unclear at present, possibly due to difficulty in measuring body weight in patients lying on the bed and insufficient clinical research. OBJECTIVES To investigate the relationship between intraoperative fluid balance and body weight change and perioperative nightly body weight change pattern throughout the hospital stay with contact-free unconstraint load cells placed under the bed legs. DESIGN Observational and exploratory study. SETTING A single university hospital. PATIENTS Twenty adult patients were undergoing elective abdominal surgery under general anaesthesia. MAIN OUTCOME MEASURES Body weight. RESULTS Immediately after surgery, body weight increased significantly by 2.7 ± 1.3 kg, equivalent to a 5% increase from the preoperative body weight. This increase was not correlated with (P = 0.178) the intraoperative fluid balance and was significantly greater than the intraoperative fluid balance 1.5 ± 0.4 kg (P &lt; 0.001). The body weight returned to the preoperative level on postoperative day (POD)3 and further significantly decreased to 97% of the preoperative body weight at POD6 (P &lt; 0.001). This physiological nocturnal weight loss pattern was maintained throughout hospitalisation except when fluid was infused. Compared with their preoperative status, patients stayed in bed longer with smaller body movements and left the bed less frequently during the daytime until POD3. Conversely, the patients had greater body movements in bed during the night leading to smaller diurnal variation in the body movements in bed after POD4. CONCLUSION Both perioperative fluid balance calculation and body weight measurement may have different but mutually complementary roles in perioperative managements. Postoperative fluid and nutrition management strategies are potentially new directions for treatment through continuous weight monitoring during the perioperative period. Trial registration : UMIN Clinical Trials Registry (UMIN000040164).