Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices.

Introduction: Algorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions. Methods: The methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation. Results: We identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia. Conclusion: Real-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.

Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.

Traditional neural network models of associative memories were used to store and retrieve static patterns. We develop reservoir-computing based memories for complex dynamical attractors, under two common recalling scenarios in neuropsychology: location-addressable with an index channel and content-addressable without such a channel. We demonstrate that, for location-addressable retrieval, a single reservoir computing machine can memorize a large number of periodic and chaotic attractors, each retrievable with a specific index value. We articulate control strategies to achieve successful switching among the attractors, unveil the mechanism behind failed switching, and uncover various scaling behaviors between the number of stored attractors and the reservoir network size. For content-addressable retrieval, we exploit multistability with cue signals, where the stored attractors coexist in the high-dimensional phase space of the reservoir network. As the length of the cue signal increases through a critical value, a high success rate can be achieved. The work provides foundational insights into developing long-term memories and itinerancy for complex dynamical patterns.

Adaptable reservoir computing: A paradigm for model-free data-driven prediction of critical transitions in nonlinear dynamical systems.

A problem in nonlinear and complex dynamical systems with broad applications is forecasting the occurrence of a critical transition based solely on data without knowledge about the system equations. When such a transition leads to system collapse, as often is the case, all the available data are from the pre-critical regime where the system still functions normally, making the prediction problem challenging. In recent years, a machine-learning based approach tailored to solving this difficult prediction problem, adaptable reservoir computing, has been articulated. This Perspective introduces the basics of this machine-learning scheme and describes representative results. The general setting is that the system dynamics live on a normal attractor with oscillatory dynamics at the present time and, as a bifurcation parameter changes into the future, a critical transition can occur after which the system switches to a completely different attractor, signifying system collapse. To predict a critical transition, it is essential that the reservoir computer not only learns the dynamical "climate" of the system of interest at some specific parameter value but, more importantly, discovers how the system dynamics changes with the bifurcation parameter. It is demonstrated that this capability can be endowed into the machine through a training process with time series from a small number of distinct, pre-critical parameter values, thereby enabling accurate and reliable prediction of the catastrophic critical transition. Three applications are presented: predicting crisis, forecasting amplitude death, and creating digital twins of nonlinear dynamical systems. Limitations and future perspectives are discussed.

Outcomes after fertility-sparing surgery of early-stage ovarian cancer: A nationwide population-based study.

BACKGROUND: Fertility-sparing surgery (FSS) is an alternative choice of young patients who have not completed their family planning and still have fertility needs. The aims of this study were to compare the outcomes of early-stage epithelial ovarian cancer (EOC) patients undergoing FSS and radical comprehensive staging surgery (RCS), and the suitability of FSS. METHODS: A total of 1297 patients aged between 20 and 44 years with newly diagnosed early-stage EOC were recruited from the Taiwan Cancer Registry database between 2009 and 2017. Site-specific surgery codes were used to distinguish patients in FSS group or RCS group. Cancer-specific survival (CSS) was evaluated using Kaplan-Meier method with log-rank test and Cox regression model. RESULTS: There were 401 and 896 patients in FSS and RCS group. Patients in FSS group were with younger age and mostly had Stage I disease. In contrast, patients in RCS group were older. There were more Stage II, high-grade (Grade 3) disease, and adjuvant chemotherapy in RCS group. Stage and tumor grade were two independent factors correlating with CSS and the type of surgery showed no effect on CSS (HR: 1.09, 95% CI: 0.66-1.77, p = 0.73) in multivariable analysis. In multivariable analysis, the clear cell carcinoma group who underwent FSS demonstrated better CSS compared to those in the RCS group (HR: 0.28, 95% CI: 0.06-0.82, p = 0.04). A total of 17 women who underwent FSS developed second malignancies of the uterine corpus or contralateral ovary. CONCLUSION: FSS can be a safe alternative procedure in selected young patients of Stage I EOC who have fertility desire. Endometrial biopsy before or during FSS and regular surveillance to detect recurrence are mandatory for ovarian cancer patients undergoing FSS.

Folding State within a Hysteresis Loop: Hidden Multistability in Nonlinear Physical Systems.

Identifying hidden states in nonlinear physical systems that evade direct experimental detection is important as disturbances and noises can place the system in a hidden state with detrimental consequences. We study a cavity magnonic system whose main physics is photon and magnon Kerr effects. Sweeping a bifurcation parameter in numerical experiments (as would be done in actual experiments) leads to a hysteresis loop with two distinct stable steady states, but analytic calculation gives a third folded steady state "hidden" in the loop, which gives rise to the phenomenon of hidden multistability. We propose an experimentally feasible control method to drive the system into the folded hidden state. We demonstrate, through a ternary cavity magnonic system and a gene regulatory network, that such hidden multistability is in fact quite common. Our findings shed light on hidden dynamical states in nonlinear physical systems which are not directly observable but can present challenges and opportunities in applications.

Polygenic Risk Score Predicts Earlier-Onset Adult Systemic Lupus Erythematosus and First-Year Renal Diseases in a Taiwanese Cohort.

OBJECTIVES: This study aimed to develop a predictive model using polygenic risk score (PRS) to forecast renal outcomes for adult systemic lupus erythematosus (SLE) in a Taiwanese population. METHODS: Patients with SLE (n=2782) and matched non-SLE controls (n=11 128) were genotyped using Genome-Wide TWB 2.0 single-nucleotide polymorphism (SNP) array. PRS models (C+T, LDpred2, Lassosum, PRSice-2, PRS-continuous shrinkage (CS)) were constructed for predicting SLE susceptibility. Logistic regression was assessed for C+T-based PRS association with renal involvement in patients with SLE. RESULTS: In the training set, C+T-based SLE-PRS, only incorporating 27 SNPs, outperformed other models with area under the curve (AUC) values of 0.629, surpassing Lassosum (AUC=0.621), PRSice-2 (AUC=0.615), LDpred2 (AUC=0.609) and PRS-CS (AUC=0.602). Additionally, C+T-based SLE-PRS demonstrated consistent predictive capacity in the testing set (AUC=0.620). Individuals in the highest quartile exhibited earlier SLE onset (39.06 vs 44.22 years, p<0.01), higher Systemic Lupus Erythematosus Disease Activity Index scores (3.00 vs 2.37, p=0.04), elevated risks of renal involvement within the first year of SLE diagnosis, including WHO class III-IV lupus nephritis (OR 2.36, 95% CI 1.47 to 3.80, p<0.01), estimated glomerular filtration rate <60 mL/min/1.73m2 (OR 1.49, 95% CI 1.18 to 1.89, p<0.01) and urine protein-to-creatinine ratio >150 mg/day (OR 2.07, 95% CI 1.49 to 2.89, p<0.01), along with increased seropositivity risks, compared with those in the lowest quartile. Furthermore, among patients with SLE with onset before 50 years, the highest PRS quartile was significantly associated with more serious renal diseases within the first year of SLE diagnosis. CONCLUSIONS: PRS of SLE is associated with earlier onset, renal involvement within the first year of SLE diagnosis and seropositivity in Taiwanese patients. Integrating PRS with clinical decision-making may enhance lupus nephritis screening and early treatment to improve renal outcomes in patients with SLE.

Deep-learning reconstruction of complex dynamical networks from incomplete data.

Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

Effectiveness of the Huddles in Improving the Patient Safety Attitudes Among Clinical Team Members.

BACKGROUND AND OBJECTIVES: Huddles among members of interdisciplinary medical teams involve short stand-up sessions and allow team members to focus on existing or emerging patient safety issues, thereby facilitating team communication. Hospital managers are able to recognize the current situation of the organization through patient safety attitudes, strengthen team members' awareness of patient safety, and improve the quality of health care. The purpose of this study was to determine the effects of huddles on improving team members' attitudes toward patient safety. METHODS: We used a quasi-experimental design and selected 2 adult wards with similar properties as the experimental and comparison groups by convenience sampling. Data collection was from December 1, 2021, to June 30, 2022, at a teaching hospital in central Taiwan. Team members of the ward performing huddles formed the experimental group, and they participated 2 times per week in 15-minute huddles from 8:15 to 8:30 am for a total of 4 weeks. The comparison group adopted the routine team care process. Both groups completed the Safety Attitudes Questionnaire during the pre- and post-tests of the study. RESULTS: The experimental group scored significantly higher in the post-test than in the pre-test in all aspects of safety attitudes, with the exception of stress recognition. These improved aspects were teamwork climate (76.47 ± 15.90 vs 83.29 ± 13.52, P < .001), safety climate (75.94 ± 16.14 vs 82.81 ± 13.74, P < .001), job satisfaction (74.34 ± 20.22 vs 84.40 ± 17.22, P <.001), perceptions of management (78.02 ± 19.99 vs 85.51 ± 15.97, P < .001), and working conditions (78.85 ± 17.87 vs 86.81 ± 14.74, P < .001). CONCLUSION: Through the huddles, clinical team members improved their understanding of different aspects of safety attitudes. Such a study provided ward units with real-time improvement and adjustment in terms of patient safety during their medical work processes with better patient safety.

[Assessment of Microplastic Pollution and Estimation of Annual Emission Volume in the Dongshan Canal of Yichang City].

Microplastics, as an emerging pollutant, have garnered global attention. Urban areas are key hotspots for the generation of microplastic pollution, whereas urban water bodies act as vital conduits for the dissemination of microplastics to other freshwater environments. In this study, the Dongshan Canal in the urban area of Yichang City was selected as the research subject. Through field sampling, microscopic observation, and Fourier infrared spectroscopy analysis conducted in July and October 2022, the occurrence characteristics and potential pollution sources of microplastics in the water body of the Dongshan Canal were identified and analyzed. The ecological risk and annual emission volume of microplastics in the water body were quantitatively assessed using the risk index (H), pollution load index (PLI) model, and proportional flow method. The results indicated that the average abundances of microplastics in the surface water of the Dongshan Canal were (7 295±1 051) n·m-3 (July) and (5 145±762.6) n·m-3 (October). Fibrous microplastics (27.63%-63.23%), microplastics with a size of <0.5 mm (75.68%-96.2%), and colored microplastics (22.73%-61.83%) dominated the samples, with PE (30.1%) and PET (26.33%) being the predominant materials. The assessment results from the two models classified the ecological risk index of the Dongshan Canal as class Ⅲ, whereas the overall pollution load fell into class I, with certain sampling points reaching class Ⅱ. Estimates revealed that the Dongshan Canal transports approximately 3.37 t of microplastics to the Yangtze River annually. Overall, the microplastic pollution level in the Dongshan Canal of Yichang City could be considered moderate, with potential sources of pollution including laundry wastewater, personal care products, and plastic waste.

[Occurrence Characteristics of Microplastics in Multi-environmental Media and Bellamya aeruginosa of Manao River].

Microplastic pollution poses threats to aquatic ecosystems and human health. In this study, in order to investigate the characteristics of microplastic occurrence in different environmental media, the abundance, particle size, shape, color, and composition types of microplastics in the water column, sediment, riparian zone soil, and the benthic snail Bellamya aeruginosa of the Manao River were analyzed using field sampling, microscopic observation, and Fourier infrared spectroscopy. The results showed that the average abundance of microplastics in the surface water of the Manao River was (5.9±0.26) n·L-1; the abundance of microplastics in the upper sediment (by dry weight) was (1.35±0.1) n·g-1, and that in the lower sediment (by dry weight) was (0.93±0.12) n·g-1. The abundance of microplastics in the near riparian zone soil (by dry weight) was (0.68±0.16) n·g-1, and that in the far riparian zone soil (by dry weight) was (0.69±0.14) n·g-1, and the abundance of microplastics in the B. aeruginosa was (2.06±0.25) n·g-1. The analysis results showed that the abundance of microplastics in the upper and lower sediments were positively correlated; the abundance of microplastics in B. aeruginosa was positively correlated with the abundance of microplastics in the upper and lower sediments, respectively; and the abundance of microplastics in the near and far riparian zone soils were also correlated. Most of the microplastics within each environmental medium and B. aeruginosa were <0.1 mm in size, mainly in the form of fibers and fragments, mainly blue and black in color, and mainly composed of polypropylene (PP) and polyethylene (PE). It was found that microplastics in riparian zone soils mainly originated from the fragmentation and decomposition of agricultural plastic films. The results of this study shed light on the accumulation of microplastics in macrobenthic organisms through the investigation of microplastics in multi-environmental media and in the B. aeruginosa, which helps us to understand the potential ecological risk of microplastics in a comprehensive manner.

The Role of Chitinase-3-like Protein-1 (YKL40) in the Therapy of Cancer and Other Chronic-Inflammation-Related Diseases.

Chitinase-3-like protein-1 (CHI3L1), also known as YKL40, is a glycoprotein that belongs to the chitinase protein family. It is involved in various biological functions, including cell proliferation and tissue remodeling, with inflammatory and immunomodulatory capabilities. Several studies have shown that CHI3L1(YKL40) is upregulated in various diseases, such as cancer, asthma, and inflammatory bowel disease, among others. Although the expression level of CHI3L1(YKL40) is associated with disease activity, severity, and prognosis, its potential as a therapeutic target is still under investigation. In this review, we summarize the biological functions, pathological roles, and potential clinical applications of specific inhibitors and targeted therapies related to CHI3L1(YKL40).

Kefir peptides mitigate bleomycin-induced pulmonary fibrosis in mice through modulating oxidative stress, inflammation and gut microbiota.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and life-threatening lung disease with high mortality rates. The limited availability of effective drugs for IPF treatment, coupled with concerns regarding adverse effects and restricted responsiveness, underscores the need for alternative approaches. Kefir peptides (KPs) have demonstrated antioxidative, anti-inflammatory, and antifibrotic properties, along with the capability to modulate gut microbiota. This study aims to investigate the impact of KPs on bleomycin-induced pulmonary fibrosis. METHODS: Mice were treated with KPs for four days, followed by intratracheal injection of bleomycin for 21 days. Comprehensive assessments included pulmonary functional tests, micro-computed tomography (µ-CT), in vivo image analysis using MMPsense750, evaluation of inflammation- and fibrosis-related gene expression in lung tissue, and histopathological examinations. Furthermore, a detailed investigation of the gut microbiota community was performed using full-length 16 S rRNA sequencing in control mice, bleomycin-induced fibrotic mice, and KPs-pretreated fibrotic mice. RESULTS: In KPs-pretreated bleomycin-induced lung fibrotic mice, notable outcomes included the absence of significant bodyweight loss, enhanced pulmonary functions, restored lung tissue architecture, and diminished thickening of inter-alveolar septa, as elucidated by morphological and histopathological analyses. Concurrently, a reduction in the expression levels of oxidative biomarkers, inflammatory factors, and fibrotic indicators was observed. Moreover, 16 S rRNA sequencing demonstrated that KPs pretreatment induced alterations in the relative abundances of gut microbiota, notably affecting Barnesiella_intestinihominis, Kineothrix_alysoides, and Clostridium_viride. CONCLUSIONS: Kefir peptides exerted preventive effects, protecting mice against bleomycin-induced lung oxidative stress, inflammation, and fibrosis. These effects are likely linked to modifications in the gut microbiota community. The findings highlight the therapeutic potential of KPs in mitigating pulmonary fibrosis and advocate for additional exploration in clinical settings.

Aldo-keto reductase family 1 member A1 (AKR1A1) exerts a protective function in alcohol-associated liver disease by reducing 4-HNE accumulation and p53 activation.

BACKGROUND: The development of alcohol-associated liver disease (ALD) is influenced by the amount and duration of alcohol consumption. The resulting liver damage can range from reversible stages, such as steatosis, steatohepatitis and alcoholic fibrosis, to the advanced and irreversible stage of cirrhosis. Aldo-keto reductase family 1 member A1 (AKR1A1) is a member of the aldo-keto reductase family that catalyzes the reduction of aldehyde groups to their corresponding alcohols in an NADPH-dependent manner. AKR1A1 was found to be downregulated in patients diagnosed with ALD. This study aims to interpret the protective effects of AKR1A1 on the development of ALD. METHODS: A 5% alcohol-fed (AF) Akr1a1 knockout (Akr1a1-/-) mouse model and an AML12 hepatocyte model were used. The effects of AKR1A1 on liver function, inflammation, oxidative stress, lipid accumulation, and fibrosis were assessed by ELISA, western blotting, RT‒PCR, and a variety of histological staining methods in AF-induced wild-type (WT) and Akr1a1-/- mice compared to control liquid diet-fed (PF) WT and Akr1a1-/- mice. RESULTS: The results demonstrated that AF-WT mice expressed higher levels of AKR1A1 than WT mice fed a control diet, and they did not show any noticeable liver steatosis. However, AF-Akr1a1-/- mice displayed a lower survival rate and more severe liver injury than AF-WT mice, as demonstrated by increased proinflammatory cytokines, oxidative stress, lipid accumulation, fibrosis, and reduced antioxidant enzymes in their livers. Additionally, elevated levels of 4-HNE and p53 phosphorylation were observed in AF-Akr1a1-/- mice, suggesting that the loss of AKR1A1 led to increased 4-HNE accumulation and subsequent activation of p53, which contributed to the progression of ALD. Furthermore, in AML12 hepatocytes, Akr1a1 knockdown aggravated oxidative stress and steatosis induced by palmitic acid/oleic acid (P/O) inflammation induced by lipopolysaccharide (LPS), and fibrosis induced by TGF-ß1. CONCLUSIONS: This loss-of-function study suggests that AKR1A1 plays a liver-protective role during chronic alcohol consumption by reducing the accumulation of 4-HNE and inhibiting 4-HNE-mediated p53 activation.

Unlabeled data selection for active learning in image classification.

Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

Association between HNF4A rs1800961 polymorphisms and gallstones in a Taiwanese population.

BACKGROUND AND AIM: A large genetic effect of a novel gallstone-associated genetic variant, the hepatocyte nuclear factor 4α (HNF4A) rs1800961 polymorphism, has been identified through recent genome-wide association studies. However, this effect has not been validated in Asian populations. We investigated the association between the rs1800961 variant and gallstones among a Taiwanese population. METHODS: A total of 20 405 participants aged between 30 and 70 years voluntarily enrolled in the Taiwan Biobank. Self-report questionnaires, physical examinations, biochemical tests, and genotyping were used for analysis. The association of the HNF4A rs1800961 variant and other metabolic risks with gallstone disease was analyzed using multiple logistic regression models. RESULTS: The minor T allele of HNF4A rs1800961 was associated with an increased risk of gallstone, and the association remained significant even after adjustment for other risk factors including age, body mass index (BMI), diabetes, hyperlipidemia, hypertension, and cigarette smoking (adjusted odds ratio [OR] = 1.90, 95% confidence interval [CI] = 1.31 to 2.75) in male participants. When further stratified by BMI and age, the lithogenic effect was the most significant in male participants with obesity (adjusted OR = 3.55, 95% CI = 1.92 to 6.56) and who were younger (adjusted OR = 2.45, 95% CI = 1.49 to 4.04). CONCLUSION: The novel gallstone-associated HNF4A rs1800961 variant was associated with the risk of gallstone in the Taiwanese men. Screening for the rs1800961 polymorphism may be particularly useful in assessing the risk of gallstone formation in younger or obese men.

A case report on multiple acyl-CoA dehydrogenase deficiency with severe myopathy and osteoporosis.

A 35-year-old male patient presented fluctuating bilateral lower extremity weakness for 3 years. Physical examination showed grade 4 proximal muscle weakness in both lower extremities and grade 5 distal muscle weakness. Laboratory data revealed elevated creatine kinase, triglycerides, and cholesterol. Muscle pathology showed deposition of lipid droplet under the sarcolemma. Bone densitometry indicated severe osteoporosis. Next-generation sequencing revealed a pathogenic mutation in the ETFDH gene. The patient was diagnosed with late-onset multiple acyl-CoA dehydrogenase deficiency. After riboflavin treatment, symptoms of the patient were relieved, physical endurance was restored, and bone mineral density was improved.

Research Progress on Efficacy-related Biomarkers of Immunotherapy in Non-small Cell Lung Cancer / 肿瘤防治研究

Lung cancer is one of the most common cancers worldwide, and its mortality rate remains high. In addition to conventional surgery, radiotherapy, and chemotherapy, immunotherapy methods have been developed and used in recent years for the treatment of non-small cell lung cancer (NSCLC). However, only a small number of patients with NSCLC can benefit from immunotherapy strategies, and some patients even have hyperprogression after receiving immunotherapy. Therefore, precision immunotherapy requires effective biomarkers to guide it. In this paper, tissue samples, blood samples, intestinal microbiota, and other biomarkers are reviewed according to different sample sources. Blood samples, including TCR immune repertoire, Tregs cells, cytokines, lactate dehydrogenase, and other markers, are summarized and analyzed to provide reference for clinicians' diagnosis and treatment decisions.

Rate-induced tipping in complex high-dimensional ecological networks.

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.

Prognostic nutritional index and neutrophil-lymphocyte ratio predict toxicities and prognosis in patients with cervical cancer treated with curative radiochemotherapy.

BACKGROUND: This study aimed to investigate the influence of immunonutritional factors on treatment-related toxicities and survival outcomes in patients with cervical cancer undergoing definitive radiochemotherapy. METHODS: Patients with cervical cancer who received curative radiochemotherapy between 2016 and 2021 were retrospectively investigated. Pretreatment prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) were measured. Survival outcomes, acute and late toxicities were evaluated. RESULTS: Among the 138 patients, those with larger tumor diameters had significantly lower pre-treatment PNI (p = 0.005). Pre-treatment immunonutritional factors were predictive of clinical survival, whereas post-treatment factors did not correlate with prognosis. Patients with low pre-treatment PNI (<49.5) or high NLR (>2.4) had shorter progression-free survival (PFS, HR: 1.86, p = 0.045 for PNI; HR: 3.15, p = 0.002 for NLR) and overall survival (OS, HR: 1.80, p = 0.048 for PNI; HR: 3.83, p = 0.015 for NLR). High pre-treatment NLR was associated with an increased risk of acute diarrhea (p = 0.049) and late severe toxicities (p = 0.046). Combined analysis revealed that pre-treatment good nutritional status and low systemic inflammation were linked to longer PFS (p = 0.007) and OS (p = 0.002), and poor nutritional status and substantial systemic inflammation were associated with higher rates of late severe toxicities (p = 0.036), with higher prognostic value in advanced stage patients. CONCLUSIONS: Pretreatment immunonutritional measures serve as quantitative biomarkers for predicting survivals and treatment toxicities in patients with cervical cancer treated with definitive radiochemotherapy.