Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Identification of response regulation governing ozone formation based on influential factors using a random forest approach.

The pursuit of enhanced scientific, refined, and precise ozone and air quality control continues to pose significant challenges. Using data visualization techniques and random forest (RF) algorithms, the temporal distribution of atmospheric pollutants and the interrelationship between O3 concentration and its influential factors were investigated with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions predominantly belonged to NOx-sensitive and transition zone. Extremely high O3 concentration were primarily observed when temperatures (T) exceeded 30 °C, with relative humidity (RH) ranging between 30 and 60 %. NO2, RH and T were identified as the top 3 important factors, and O3 concentration have stronger linearly relationship to RH and T, while stronger nonlinearly relationship to NO2. By employing an optimized RF model, controlling consistent mild and high reaction atmospheric conditions, the O3 concentration response to the change of individual influencing factors was acquired. The O3 concentration increased and then decreased in response to the increasing NO2 concentration, displaying a characteristic inflection point at 10 µg m-3. More reactive radicals produced at higher VOCs concentration and continuing NOx cycle at lower NO2 concentration, resulting in the acceleration in the direction of producing more O3. Therefore, the significant different O3 response to variation of VOCs and NOx concentration between mild and high reaction atmospheric conditions, as well as the existing of oxidant elevation should be considered in local air quality control. This study demonstrates the efficacy of ML methods in simulating nonlinear response of O3, supports the understanding of local O3 formation and quick guidance for precise local O3 pollution control and the related strategies.

Understanding Users' Acceptance of Artificial Intelligence Applications: A Literature Review.

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.

Effects of multiple protection model in the operating room on physiological stress and risk events in patients undergoing coronary artery stent implantation.

OBJECTIVE: To analyze the impact of multiple protection model in the operating room on patients' physiological stress and risk events after coronary artery stent implantation (CASI). METHODS: During October 2021 to October 2022, 150 patients with coronary heart disease (CHD) were picked as the research subjects, all of whom underwent CASI. The clinical data were retrospectively analyzed, and the patients were divided into two groups according to different nursing methods, with 75 cases in each group. Patients in the intervention group received multiple protection model intervention in the operating room, and the patients in the control group adopted conventional care model. The patient satisfaction with nursing, postoperative recovery, psychological stress scores, physiological stress indicators, and adverse cardiac risk events were recorded. RESULTS: Patients in the intervention group had much higher percentage of the patient satisfaction with nursing than those in the control group (P < 0.05). The time to get out of bed and hospital stay was significantly shorter and the 6-min walking distance was markedly longer in the intervention group than the control (P < 0.05). The Hamilton Anxiety (HAMA) scale and Hamilton Depression (HAMD) scale score of patients in two groups were sharply decreased after the intervention (P < 0.05), which were strongly lower in the intervention group than the control (P < 0.001). After the intervention, the heart rate, cortisol and epinephrine of patients were all sensibly elevated in two groups (P < 0.05), which were all memorably lower in the intervention group than the control (P < 0.001). The incidence of adverse cardiac risk events in the intervention group was 5.33%, which was dramatically lower than 16.00% in the control group (P < 0.05). CONCLUSION: The application of multiple protection model in the operating room on patients undergoing coronary stent implantation promoted postoperative recovery, reduced patients' psychological and physiological stress, maintained blood pressure and other vital signs, reduced the incidence of adverse cardiac risk events, and improved the patient satisfaction with nursing.

Serine Rejuvenated Degenerated Volvariella volvacea by Enhancing ROS Scavenging Ability and Mitochondrial Function.

Serine is a functional amino acid that effectively regulates the physiological functions of an organism. This study investigates the effects of adding exogenous serine to a culture medium to explore a feasible method for the rejuvenation of V. volvacea degenerated strains. The tissue isolation subcultured strains T6, T12, and T19 of V. volvacea were used as test strains, and the commercially cultivated strain V844 (T0) was used as a control. The results revealed that the addition of serine had no significant effect on non-degenerated strains T0 and T6, but could effectively restore the production characteristics of degenerated strains T12 and T19. Serine increased the biological efficiency of T12 and even helped the severely degenerated T19 to regrow its fruiting body. Moreover, exogenous serine up-regulated the expression of some antioxidant enzyme genes, improved antioxidase activity, reduced the accumulation of reactive oxygen species (ROS), lowered malondialdehyde (MDA) content, and restored mitochondrial membrane potential (MMP) and mitochondrial morphology. Meanwhile, serine treatment increased lignocellulase and mycelial energy levels. These findings form a theoretical basis and technical support for the rejuvenation of V. volvacea degenerated strains and other edible fungi.

Selective lead capture using amide-containing COFs: A novel strategy for efficient soil remediation.

A critical consideration in the application of phytoremediation to remediate sludge soil contaminated with heavy metals is the potential for leaching risks that prevail prior to the efficient uptake of these metals by plants. The most cost-effective method is to use heavy metal stabilizers with selective adsorption. A novel amide-based COF material (COF-TH) has been synthesized as a heavy metal stabilizer for Pb. COF-TH exhibits significant selectivity for Pb in five-metal-mixed solutions, with a distribution coefficient KD as high as 3279 mL·g-1, which was more than 7.3 times that of other heavy metals. The maximum adsorption capacity of COF-TH for Pb was 189 mg·g-1. The adsorption fitted Langmuir model and intra-particle diffusion model, and satisfied pseudo-second-order kinetic model. The excellent selectivity and adsorption performance originate from the complexation between abundant amide groups and Pb ions. Pot experiments and leaching assays confirm that COF-TH decreased Pb leachate concentrations by 77.8 % without significantly decreasing total phytoextracted amounts of other heavy metals, due to the high selectivity of COF-TH to Pb. Additionally, its positive impact on plant growth and microbial diversity makes it a promising soil remediation agent. This investigation offers a novel approach to mitigate the leaching risk of a specific heavy metal Pb during sludge land application by integrating soil phytoremediation with stabilization techniques.

Operation room nursing based on humanized nursing mode combined with nitric oxide on rehabilitation effect after lung surgery.

BACKGROUND: With advancements in the diagnosis and treatment of lung diseases, lung segment surgery has become increasingly common. Postoperative rehabilitation is critical for patient recovery, yet challenges such as complications and adverse outcomes persist. Incorporating humanized nursing modes and novel treatments like nitric oxide inhalation may enhance recovery and reduce postoperative complications. AIM: To evaluate the effects of a humanized nursing mode combined with nitric oxide inhalation on the rehabilitation outcomes of patients undergoing lung surgery, focusing on pulmonary function, recovery speed, and overall treatment costs. METHODS: A total of 79 patients who underwent lung surgery at a tertiary hospital from March 2021 to December 2021 were divided into a control group (n = 39) receiving a routine nursing program and an experimental group (n = 40) receiving additional humanized nursing interventions and atomized inhalation of nitric oxide. Key indicators were compared between the two groups alongside an analysis of treatment costs. RESULTS: The experimental group demonstrated significant improvements in pulmonary function, reduced average recovery time, and lower total treatment costs compared to the control group. Moreover, the quality of life in the experimental group was significantly better in the 3 months post-surgery, indicating a more effective rehabilitation process. CONCLUSION: The combination of humanized nursing mode and nitric oxide inhalation in postoperative care for lung surgery patients significantly enhances pulmonary rehabilitation outcomes, accelerates recovery, and reduces economic burden. This approach offers a promising reference for improving patient care and rehabilitation efficiency following lung surgery.

Emerging perspectives on precision therapy for Parkinson's disease: multidimensional evidence leading to a new breakthrough in personalized medicine.

PD is a prevalent and progressive neurodegenerative disorder characterized by both motor and non-motor symptoms. Genes play a significant role in the onset and progression of the disease. While the complexity and pleiotropy of gene expression networks have posed challenges for gene-targeted therapies, numerous pathways of gene variant expression show promise as therapeutic targets in preclinical studies, with some already in clinical trials. With the recognition of the numerous genes and complex pathways that can influence PD, it may be possible to take a novel approach to choose a treatment for the condition. This approach would be based on the symptoms, genomics, and underlying mechanisms of the disease. We discuss the utilization of emerging genetic and pathological knowledge of PD patients to categorize the disease into subgroups. Our long-term objective is to generate new insights for the therapeutic approach to the disease, aiming to delay and treat it more effectively, and ultimately reduce the burden on individuals and society.

An Immunomodulator-Boosted Lactococcus Lactis Platform For Enhanced In Situ Tumor Vaccine.

In situ vaccination is an attractive type of cancer immunotherapy, and methods of persistently dispersing immune agonists throughout the entire tumor are crucial for maximizing their therapeutic efficacy. Based on the probiotics usually used for dietary supplements, an immunomodulator-boosted Lactococcus lactis (IBL) strategy is developed to enhance the effectiveness of in situ vaccination with the immunomodulators. The intratumoral delivery of OX40 agonist and resiquimod-modified Lactococcus lactis (OR@Lac) facilitates local retention and persistent dispersion of immunomodulators, and dramatically modulates the key components of anti-tumor immune response. This novel vaccine activated dendritic cells and cytotoxic T lymphocytes in the tumor and tumor-draining lymph nodes, and ultimately significantly inhibited tumor growth and prolonged the survival rate of tumor-bearing mice. The combination of OR@Lac and ibrutinib, a myeloid-derived suppressor cell inhibitor, significantly alleviated or even completely inhibited tumor growth in tumor-bearing mice. In conclusion, IBL is a promising in situ tumor vaccine approach for clinical application and provides an inspiration for the delivery of other drugs.

FLASH Radiotherapy: Mechanisms of Biological Effects and the Therapeutic Potential in Cancer.

Radiotherapy is an important treatment for many unresectable advanced malignant tumors, and radiotherapy-associated inflammatory reactions to radiation and other toxic side effects are significant reasons which reduce the quality of life and survival of patients. FLASH-radiotherapy (FLASH-RT), a prominent topic in recent radiation therapy research, is an ultra-high dose rate treatment known for significantly reducing therapy time while effectively targeting tumors. This approach minimizes radiation side effects on at-risk organs and maximally protects surrounding healthy tissues. Despite decades of preclinical exploration and some notable achievements, the mechanisms behind FLASH effects remain debated. Standardization is still required for the type of FLASH-RT rays and dose patterns. This review addresses the current state of FLASH-RT research, summarizing the biological mechanisms behind the FLASH effect. Additionally, it examines the impact of FLASH-RT on immune cells, cytokines, and the tumor immune microenvironment. Lastly, this review will discuss beam characteristics, potential clinical applications, and the relevance and applicability of FLASH-RT in treating advanced cancers.

A pan-KRAS degrader for the treatment of KRAS-mutant cancers.

KRAS mutations are highly prevalent in a wide range of lethal cancers, and these mutant forms of KRAS play a crucial role in driving cancer progression and conferring resistance to treatment. While there have been advancements in the development of small molecules to target specific KRAS mutants, the presence of undruggable mutants and the emergence of secondary mutations continue to pose challenges in the clinical treatment of KRAS-mutant cancers. In this study, we developed a novel molecular tool called tumor-targeting KRAS degrader (TKD) that effectively targets a wide range of KRAS mutants. TKD is composed of a KRAS-binding nanobody, a cell-penetrating peptide selectively targeting cancer cells, and a lysosome-binding motif. Our data revealed that TKD selectively binds to KRAS in cancer cells and effectively induces KRAS degradation via a lysosome-dependent process. Functionally, TKD suppresses tumor growth with no obvious side effects and enhances the antitumor effects of PD-1 antibody and cetuximab. This study not only provides a strategy for developing drugs targeting "undruggable" proteins but also reveals that TKD is a promising therapeutic for treating KRAS-mutant cancers.

Machine Learning for Prediction of Non-Small Cell Lung Cancer Based on Inflammatory and Nutritional Indicators in Adults: A Cross-Sectional Study.

Purpose: The aim of this study was to evaluate the potential benefit of blood inflammation in the diagnosis of non-small cell lung cancer (NSCLC) and propose a machine-learning-based method to predict NSCLC in asymptomatic adults. Patients and Methods: A cross-sectional study was evaluated using medical records of 139 patients with non-small cell lung cancer and physical examination data from May 2022 to May 2023 of 198 healthy controls. The NSCLC cohort comprised 128 cases of adenocarcinoma, 3 cases of squamous cell carcinoma, and 8 cases of other NSCLC subtypes. The correlation between inflammatory and nutritional markers, such as monocytes, neutrophils, LMR, NLR, PLR, PHR and non-small cell lung cancer was examined. Features were selected using Python's feature selection library and analyzed by five algorithms. The predictive ability of the model for non-small cell lung cancer diagnosis was assessed by precision, accuracy, recall, F1 score, and area under the curve (AUC). Results: The results showed that the top 14 important factors were PDW, age, TP, RBC, HGB, LYM, LYM%, RDW, PLR, LMR, PHR, MONO, MONO%, gender. Additionally, the naive Bayes (NB) algorithm demonstrated the highest overall performance in predicting adult NSCLC among the five machine learning algorithms, achieving an accuracy of 0.87, a macro average F1 score of 0.85, a weighted average F1 score of 0.87, and an AUC of 0.84. Conclusion: In feature ranking, platelet distribution width was the most important feature, and the NB algorithm performed best in predicting adult NSCLC diagnosis.

TPP1 is associated with risk of advanced precursors and cervical cancer survival.

It is unclear how telomere-binding protein TPP1 interacts with human telomerase reverse transcriptase (hTERT) and influences cervical cancer development and progression. This study included all eligible 156 cervical cancers diagnosed during 2003-2008 and followed up through 2014, 102 cervical intraepithelial neoplasia (CIN) patients, and 16 participants with normal cervix identified at the same period. Correlation of expression of TPP1 and hTERT in these lesions was assessed using Kappa statistics. TPP1 was knocked down by siRNA in three cervical cancer cell lines. We assessed mRNA expression using quantitative real-time polymerase chain reaction and protein expression using tissue microarray-based immunohistochemical staining. We further analyzed the impact of TPP1 expression on the overall survival of cervical cancer patients by calculating the hazard ratio (HR) with 95% confidence intervals (CIs) using the multivariable-adjusted Cox regression model. Compared to the normal cervix, high TPP1expression was significantly associated with CIN 3 and cervical cancers (P<0.001 for both). Expressions of TPP1 and hTERT were highly correlated in CIN 3 (Kappa statistics = 0.50, P = 0.005), squamous cell carcinoma (Kappa statistics = 0.22, P = 0.011), and adenocarcinoma/adenosquamous carcinoma (Kappa statistics = 0.77, P = 0.001). Mechanistically, knockdown of TPP1 inhibited the expression of hTERT in both mRNA and protein levels. High expression of TPP1 (HR = 2.61, 95% CI 1.23-5.51) and co-high expression of TPP1 and hTERT (HR = 2.38, 95% CI 1.28-4.43) were independently associated with worse survival in cervical cancer patients. TPP1 and hTERT expression was correlated and high expression of TPP1 was associated with high risk of CIN 3 and cervical cancer and could predict a worse survival in cervical cancer.

Potent antitumor activity of a bispecific T-cell engager antibody targeting the intracellular antigen KRAS G12V.

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is one of the most frequent oncogenes. However, there are limited treatment options due to its intracellular expression. To address this, we developed a novel bispecific T-cell engager (BiTE) antibody targeting HLA-A2/KRAS G12V complex and CD3 (HLA-G12V/CD3 BiTE). We examined its specific binding to tumor cells and T cells, as well as its anti-tumor effects in vivo. HLA-G12V/CD3 BiTE was expressed in Escherichia coli and its binding affinities to CD3 and HLA-A2/KRAS G12V were measured by flow cytometry, along with T-cell activation. In a xenograft pancreatic tumor model, the HLA-G12V/CD3 BiTE's anti-tumor effects were assessed through tumor growth, survival time, and safety. Our results demonstrated specific binding of HLA-G12V/CD3 BiTE to tumor cells with an HLA-A2/KRAS G12V mutation and T cells. The HLA-G12V/CD3 BiTE also activated T-cells in the presence of tumor cells in vitro. HLA-G12V/CD3 BiTE in vivo testing showed delayed tumor growth without severe toxicity to major organs and prolonged mouse survival. This study highlights the potential of constructing BiTEs recognizing an HLA-peptide complex and providing a novel therapy for cancer treatment targeting the intracellular tumor antigen.

Formyl peptide enhances cancer immunotherapy by activating antitumoral neutrophils, and T cells.

Neutrophils are heterogeneous and plastic, with the ability to polarize from antitumour to protumour phenotype and modulate tumour microenvironment components. While some advances have been made, the neutrophil-targeting therapy remains underexplored. Activation of formyl peptide receptors (FPRs) by formylated peptides is needed for local control of infection through the recruitment of activated neutrophils while the potential contribution of antitumour activity remains underexplored. Here, we demonstrate that neutrophils can be harnessed to suppress tumour growth through the action of the formyl peptide (FP) on the formyl peptide receptor (FPR). Mechanistically, FP efficiently recruits neutrophils to produce reactive oxygen species production (ROS), resulting in the direct killing of tumours. Antitumour functions disappeared when neutrophils were depleted by anti-Ly6G antibodies. Interestingly, extensive T-cell activation was observed in mouse tumours treated with FP, showing the potential to alter the immune suppressed tumour microenvironment (TME) and further sensitize mice to anti-PD1 therapy. Transcriptomic and flow cytometry analyses revealed the mechanisms of FP-sensitized anti-PD1 therapy, mainly including stimulated neutrophils and an altered immune-suppressed tumour microenvironment. Collectively, these data establish FP as an effective combination partner for sensitizing anti-PD1 therapy by stimulating tumour-infiltrated neutrophils.

Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park.

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.

Gastrointestinal cancer precursor risk and mortality in pancreatic intraductal papillary mucinous neoplasms: a nationwide cohort study.

BACKGROUND AND AIMS: Intraductal papillary mucinous neoplasm (IPMN) of the pancreas is a precursor of pancreatic cancer. While earlier research has shown a high prevalence of synchronous/metachronous extrapancreatic tumors in IPMN patients, these studies have often been small with retrospective data collection. The aim of the study was to examine absolute and relative risks of non-pancreatic gastrointestinal (GI) cancer precursors and mortality in histologically confirmed IPMN. METHODS: Through the nationwide ESPRESSO histopathology cohort, we retrieved data on IPMN between 1965 and 2016. Each index case was matched to ≤5 general population controls. Through Cox regression, we estimated hazard ratios (HRs) for future GI cancer precursors and death. RESULTS: A total of 117 patients with IPMN and 539 age- and sex-matched controls were included. Over a median of 2.1 years of follow up, we confirmed two (1.7%) incident GI cancer precursors in IPMN vs. four (0.7%) in controls, corresponding to an HR of 1.89 (95%CI = 0.34-10.55). By contrast, IPMN patients were at increased risk of death (HR 3.61 (95%CI = 1.79-7.27)). The most common cause of death in IPMN was pancreatic cancer (n = 14; 45.2% of all deaths). CONCLUSIONS: We found no association between IPMN and other GI cancer precursors. This argues against comprehensive routine surveillance for other GI cancer precursors in IPMN patients. Mortality was increased in IPMN with pancreatic cancer being the most common cause of death, indicating the need for lifelong follow up in all resected and non-resected patients with IPMN. However, results should be confirmed in larger cohorts.

Efficacy and safety of second-line therapy by S-1 combined with sintilimab and anlotinib in pancreatic cancer patients with liver metastasis: a single-arm, phase II clinical trial.

Background: Pancreatic adenocarcinoma carries a grim prognosis, and there are few recognized effective second-line treatment strategies. We attempted to evaluate the efficacy and safety of a combination of S-1, sintilimab, and anlotinib as a second-line treatment in pancreatic cancer patients with liver metastasis. Methods: Pancreatic cancer patients with liver metastases were recruited. S-1 was administered orally at 25 mg/m2 bid, anlotinib was administered orally at 12 mg qd from day 1 to day 14, and sintilimab was administered intravenously at 200 mg on day 1. This method was repeated every 21 days, and the therapeutic effect was evaluated every 3 cycles. The primary outcome was the objective response rate (ORR). Results: Overall, 23 patients were enrolled in this study of whom 19 patients had objective efficacy evaluation. The ORR was 10.5% (95% CI 0.4%-25.7%) in the evaluable population. The progression-free survival (PFS) was 3.53 (95% CI 2.50-7.50) months, and the overall survival (mOS) was 8.53 (95% CI 4.97-14.20) months. Grade 3 adverse events were 26.1%, and no grade 4 or above adverse events occurred. High-throughput sequencing was performed on the tumor tissues of 16 patients; patients with HRD-H (n = 10) had shorter PFS than those with HRD-L (n = 6) (2.43 vs. 5.45 months; P = 0.043), but there was no significant difference in OS between the two groups (4.43 vs. 9.35 months; P = 0.11). Conclusions: This study suggests the advantage of S-1 combined with sintilimab and anlotinib in extending OS as a second-line therapy in pancreatic cancer patients with liver metastasis. Clinical Trial Registration: ChiCTR2000030659.

Cancer Diagnoses After Recent Weight Loss.

Importance: Weight loss is common in primary care. Among individuals with recent weight loss, the rates of cancer during the subsequent 12 months are unclear compared with those without recent weight loss. Objective: To determine the rates of subsequent cancer diagnoses over 12 months among health professionals with weight loss during the prior 2 years compared with those without recent weight loss. Design, Setting, and Participants: Prospective cohort analysis of females aged 40 years or older from the Nurses' Health Study who were followed up from June 1978 until June 30, 2016, and males aged 40 years or older from the Health Professionals Follow-Up Study who were followed up from January 1988 until January 31, 2016. Exposure: Recent weight change was calculated from the participant weights that were reported biennially. The intentionality of weight loss was categorized as high if both physical activity and diet quality increased, medium if only 1 increased, and low if neither increased. Main Outcome and Measures: Rates of cancer diagnosis during the 12 months after weight loss. Results: Among 157 474 participants (median age, 62 years [IQR, 54-70 years]; 111 912 were female [71.1%]; there were 2631 participants [1.7%] who self-identified as Asian, Native American, or Native Hawaiian; 2678 Black participants [1.7%]; and 149 903 White participants [95.2%]) and during 1.64 million person-years of follow-up, 15 809 incident cancer cases were identified (incident rate, 964 cases/100 000 person-years). During the 12 months after reported weight change, there were 1362 cancer cases/100 000 person-years among all participants with recent weight loss of greater than 10.0% of body weight compared with 869 cancer cases/100 000 person-years among those without recent weight loss (between-group difference, 493 cases/100 000 person-years [95% CI, 391-594 cases/100 000 person-years]; P < .001). Among participants categorized with low intentionality for weight loss, there were 2687 cancer cases/100 000 person-years for those with weight loss of greater than 10.0% of body weight compared with 1220 cancer cases/100 000 person-years for those without recent weight loss (between-group difference, 1467 cases/100 000 person-years [95% CI, 799-2135 cases/100 000 person-years]; P < .001). Cancer of the upper gastrointestinal tract (cancer of the esophagus, stomach, liver, biliary tract, or pancreas) was particularly common among participants with recent weight loss; there were 173 cancer cases/100 000 person-years for those with weight loss of greater than 10.0% of body weight compared with 36 cancer cases/100 000 person-years for those without recent weight loss (between-group difference, 137 cases/100 000 person-years [95% CI, 101-172 cases/100 000 person-years]; P < .001). Conclusions and Relevance: Health professionals with weight loss within the prior 2 years had a significantly higher risk of cancer during the subsequent 12 months compared with those without recent weight loss. Cancer of the upper gastrointestinal tract was particularly common among participants with recent weight loss compared with those without recent weight loss.