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Introduction Approximately 30% of somatic hospital inpatients experience psychosocial distress, contributing to increased (re-)hospitalisation rates, treatment resistance, morbidity, and direct and indirect costs. However, such distress often remains unrecognised and unaddressed. We established ‘SomPsyNet’, a ‘stepped and collaborative care model’ (SCCM) for somatic hospital inpatients, aiming at alleviating this issue through early identification of distress and provision of appropriate care, providing problem-focused pathways and strengthening collaborative care. We report the protocol of the ‘SomPsyNet’ study, aiming to evaluate implementation and impact of the SCCM on distressed patients’ health-related quality of life. Secondary objectives include assessing efficacy of the screening procedures, influence of SCCM on other health outcomes and associated costs.Methods and analysis Our stepped wedge cluster randomised trial conducted at three tertiary hospitals comprises three conditions: treatment as usual (TAU) without screening for distress (phase 0), TAU with screening but without consequences (phase I, main comparator) and TAU with screening and psychosomatic-psychiatric consultations for those distressed (phase II). The time-of-transition between phases I and II was randomised. Sample size target is N=2200–2500 participants, with 6 month follow-up for distressed (anticipated n=640–700) and a subsample of non-distressed (anticipated n=200) patients. Primary outcome is mental health-related quality of life (SF-36 ‘Mental Health Component Summary score’); secondary outcomes include psychosocial distress, anxiety, depressive and somatic symptoms, symptom burden and distress, resilience, social support and qualitative of life, assessed by internationally accepted instruments, with good psychometric properties. Further, health claims data will be used to assess SCCM’s impact on direct and indirect costs.Ethics and dissemination SomPsyNet adheres to the Helsinki Declaration and is approved by the ‘Ethikkommission Nordwest- und Zentralschweiz’ (2019–01724). Findings will be published in peer-reviewed journals and communicated to participants, healthcare professionals and the public.Trial registration number Swiss National Clinical Trials Portal; ClinicalTrials.gov (NCT04269005, updated 19.09.2023).

Background: The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging., Objectives: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction., Setting: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453., Methods: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m 2 ., Results: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making., Conclusion: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies., (Copyright © 2024 American Society for Metabolic and Bariatric Surgery. Published by Elsevier Inc. All rights reserved.)

Purpose: Virtual reality (VR) allows for an immersive and interactive analysis of imaging data such as computed tomography (CT) and magnetic resonance imaging (MRI). The aim of this study is to assess the comprehensibility of VR anatomy and its value in assessing resectability of pancreatic ductal adenocarcinoma (PDAC)., Methods: This study assesses exposure to VR anatomy and evaluates the potential role of VR in assessing resectability of PDAC. Firstly, volumetric abdominal CT and MRI data were displayed in an immersive VR environment. Volunteering physicians were asked to identify anatomical landmarks in VR. In the second stage, experienced clinicians were asked to identify vascular involvement in a total of 12 CT and MRI scans displaying PDAC (2 resectable, 2 borderline resectable, and 2 locally advanced tumours per modality). Results were compared to 2D standard PACS viewing., Results: In VR visualisation of CT and MRI, the abdominal anatomical landmarks were recognised by all participants except the pancreas (30/34) in VR CT and the splenic (31/34) and common hepatic artery (18/34) in VR MRI, respectively. In VR CT, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 22/24, 20/24 and 19/24 scans, respectively. Whereas, in VR MRI, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 19/24, 19/24 and 21/24 scans, respectively. Interobserver agreement as measured by Fleiss κ was 0.7 for CT and 0.4 for MRI, respectively (p < 0.001). Scans were significantly assessed more accurately in VR CT than standard 2D PACS CT, with a median of 5.5 (IQR 4.75-6) and a median of 3 (IQR 2-3) correctly assessed out of 6 scans (p < 0.001)., Conclusion: VR enhanced visualisation of abdominal CT and MRI scan data provides intuitive handling and understanding of anatomy and might allow for more accurate staging of PDAC and could thus become a valuable adjunct in PDAC resectability assessment in the future., (© 2024. The Author(s).)

Background: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data., Methods: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation., Results: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02)., Conclusion: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use., (© 2024. The Author(s).)

Background: Artificial Intelligence provides numerous applications in the healthcare sector. The main aim of this study is to evaluate the extent of the current application of artificial intelligence in thyroid diagnostics., Methods: Our protocol was based on the Scoping Reviews extension of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-ScR). Information was gathered from PubMed, Cochrane, and EMBASE databases and Google Scholar. Eligible studies were published between 2017 and 2022., Results: The search identified 133 records, after which 18 articles were included in the scoping review. All the publications were journal articles and discussed various ways that specialists in thyroid diagnostics and surgery have utilized artificial intelligence in their practice., Conclusions: The development and incorporation of Artificial Intelligence applications in thyroid diagnostics and surgery has been moderate yet promising. However, applications are currently inconsistent and further research is needed to delineate the true benefit and limitations in this field., Competing Interests: Declaration of competing interest The authors have no competing interests to declare, and the authors did not display any conflicting interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The integration of virtual reality (VR) in surgery has gained prominence as VR applications have increased in popularity., Methods: A scoping review was undertaken, gathering the most relevant sources, utilising a detailed literature search of medical and academic databases including EMBASE, PubMed, Cochrane, IEEE, Google Scholar, and the Google search engine., Results: Of the 18 articles included, 7 focused on VR in colon surgery, 5 addressed VR in pancreas surgery, and the remaining 6 concentrated on VR in liver surgery. All the articles concluded that VR has a promising future in abdominal surgery by facilitating precision, visualisation, and surgeon training., Conclusions: Adopting VR technology in abdominal surgery has the potential to improve preoperative planning, decrease perioperative anxiety among patients, and facilitate the training of surgeons, residents, and medical students. Additional supporting studies are necessary before VR can be widely implemented in surgical care delivery., (© 2024 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery published by John Wiley & Sons Ltd.)

The primary goal of this study is to assess current patient information available on the internet concerning robotic colorectal surgery. Acquiring this information will aid in patients understanding of robotic colorectal surgery. Data was acquired through a web-scraping algorithm. The algorithm used two Python packages: Beautiful Soup and Selenium. The long-chain keywords incorporated into Google, Bing and Yahoo search engines were 'Da Vinci Colon-Rectal Surgery', 'Colorectal Robotic Surgery' and 'Robotic Bowel Surgery'. 207 websites resulted, were sorted and evaluated according to the ensuring quality information for patients (EQIP) score. Of the 207 websites visited, 49 belonged to the subgroup of hospital websites (23.6%), 46 to medical centers (22.2%), 45 to practitioners (21.7%), 42 to health care systems (20,2%), 11 to news services (5.3%), 7 to web portals (3.3%), 5 to industry (2.4%), and 2 to patient groups (0.9%). Only 52 of the 207 websites received a high rating. The quality of available information on the internet concerning robotic colorectal surgery is low. The majority of information was inaccurate. Medical facilities involved in robotic colorectal surgery, robotic bowel surgery and related robotic procedures should develop websites with credible information to guide patient decisions.

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022-00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Rapid triage of blunt agonal trauma patients is necessary to maximize survival, but autopsy is uncommon, slow, and rarely informs resuscitation guidelines. Postmortem computed tomography (PMCT) can serve as an adjunct to autopsy in guiding blunt agonal trauma resuscitation., Methods: Retrospective cohort review of trauma decedents who died at or within 1 hour of arrival following blunt trauma and underwent noncontrasted PMCT. Primary outcome was the prevalence of mortal injury defined as potential exsanguination (e.g., cavitary injury, long bone and pelvic fractures), traumatic brain injury, and cervical spine injury. Secondary outcomes were potentially mortal injuries (e.g., pneumothorax) and misplacement airway devices. Patients were grouped by whether arrest occurred prehospital/in-hospital. Univariate analysis was used to identify differences in injury patterns including multiple-trauma injury patterns., Results: Over a 9-year period, 80 decedents were included. Average age was 48.9 ± 21.7 years, 68% male, and an average ISS of 42.3 ± 16.3. The most common mechanism was motor vehicle accidents (67.5%) followed by pedestrian struck (15%). Of all decedents, 62 (77.5%) had traumatic arrest prehospital while 18 (22.5%) arrived with pulse. Between groups there were no significant differences in demographics including ISS. The most common mortal injuries were traumatic brain injury (40%), long bone fractures (25%), moderate/large hemoperitoneum (22.5%), and cervical spine injury (25%). Secondary outcomes included moderate/large pneumothorax (18.8%) and esophageal intubation rate of 5%. There were no significant differences in mortal or potentially mortal injuries, and no differences in multiple-trauma injury patterns., Conclusion: Fatal blunt injury patterns do not vary between prehospital and in-hospital arrest decedents. High rates of pneumothorax and endotracheal tube misplacement should prompt mandatory chest decompression and confirmation of tube placement in all blunt arrest patients., Level of Evidence: Prognostic and Epidemiological; Level IV., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Machine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery., Methods: We integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included., Results: A search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022., Conclusion: The integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Taha, Taha-Mehlitz, Ortlieb, Ochs, Honaker, Rosenberg, Lock, Bolli and Cattin.)

Purpose: Neuropathic pain is a complication after groin hernia surgery. Triple neurectomy of the iliohypogastric nerve, ilioinguinal nerve and genitofemoral nerve is an efficient treatment modality, with several surgical approaches. The minimally invasive endoscopic method to neurectomy was specifically investigated in this meta-analysis. Our aim is to determine the efficacy of this method in the treatment of chronic neuropathic pain posthernia repair surgery., Methods: A systematic review was conducted using four databases to search for the keywords ("endoscopic retroperitoneal neurectomy" and "laparoscopic retroperitoneal neurectomy"). The NCBI National Library of Medicine, Cochrane Library, MEDLINE Complete and BioMed Central were last searched on 26 May 2022. Randomised control trials and retrospective or prospective papers involving endoscopic retroperitoneal neurectomy operations after inguinal hernia repair were included. All other surgeries, procedures and study designs were excluded. The internal quality of included studies was assessed using the Newcastle-Ottawa Scale. The percentage of patients who had reduction in pain ("positive treatment outcome") was used to assess the procedure's effectiveness in each analysis., Results: Five comparable endoscopic retroperitoneal neurectomy studies with a total of 142 patients were analysed. Both the Wald test (Q (6) = 1.79, = .775) and the probability ratio test (Q (6) = 4.24, = .374) provide similar findings (0.000, 0.0% [0.0%; 78%]). The meta-analysis' key finding is that the intervention was up to 78% effective (95% confidence interval, 71%; 84%)., Conclusion: Endoscopic retroperitoneal neurectomy can be an effective treatment option for postoperative neuropathic pain relief following surgical hernia repair. Although there is limited reported experience with this technique, it may provide a clinical benefit to the patient. We recommend further prospective data and long-term follow-up studies be conducted to confirm and expand on these outcomes., (© 2023. The Author(s).)

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%-25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Taha, Taha-Mehlitz, Ochs, Enodien, Honaker, Frey and Cattin.)

Background: Complications in colon surgery can have severe health consequences, while at the same time, they are associated with increased costs. An anastomotic leak (AL) is associated with significantly increased costs compared to cases without. The aim of our analysis was to evaluate, which individual processes and patient-unrelated factors influencing the treatment process of colon surgery are responsible for the financial burden in patients with AL. Methods: Data from 263 patients who underwent colon surgery in Wetzikon hospital between January 2018 and December 2020 and was analyzed. In these 263 cases, 12 anastomotic leaks occurred and were compared with 36 cases without AL using a Propensity Score Matching (PSM). The covariates for the PSM have been Age, Sex, and Type of Surgery (t value: −3.26, p-value: 0.001). Results: A total of 48 surgeries were broken down in terms of costs and profitability. This reflected a mean deficit of −37,527 CHF per case (range from −130.05 to +755 CHF) for patients with AL, whereas a mean profit of 1590 CHF per case (range from −24.37 to +12.65 CHF) for those without AL (p < 0.001). Thus, the difference in profit showed a factor of 24.6 with an overall significant negative outcome for the occurrence of AL. The main cost contributing factors were the length of hospital stay (~p < 0.05) and length of intensive care (p < 0.05), whereas neither surgical operation time and anesthesia time nor surgical access, insurance status, indication or type of operation had a significant influence on the net revenue. Conclusion: AL after colon surgery leads to a significant deficit regarding the net revenue. Regarding process optimization, our analysis identified several sectors of non-patient-related, yet cost-influencing variables that should be addressed in future evaluations and optimization of the colon surgery treatment processes.

Objective: The use of machine learning (ML) has revolutionized every domain of medicine. Surgeons are now using ML models for disease detection and outcome prediction with high precision. ML-guided colorectal surgeries are more efficient than conventional surgical procedures. The primary aim of this paper is to provide an overview of the latest research on "ML in colorectal surgery", with its viable applications., Methods: PubMed, Google Scholar, Medline, and Cochrane library were searched., Results: After screening, 27 articles out of 172 were eventually included. Among all of the reviewed articles, those found to fit the criteria for inclusion had exclusively focused on ML in colorectal surgery, with justified applications. We identified existing applications of ML in colorectal surgery. Additionally, we discuss the benefits, risks, and safety issues., Conclusions: A better, more sustainable, and more efficient method, with useful applications, for ML in surgery is possible if we and data scientists work together to address the drawbacks of the current approach. Potential problems related to patients' perspectives also need to be resolved. The development of accurate technologies alone will not solve the problem of perceived unreliability from the patients' end. Confidence can only be developed within society if more research with precise results is carried out.

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

Background: This study aimed to compare property development and increasing investment in real estate by the healthcare system organizations in the USA and Europe. Real estate investments have upsurged in healthcare due to the multiple benefits to patients and medical practitioners., Methods: The approach of acquiring data was through secondary sources and online questionnaires. The researchers applied inclusion and exclusion criteria by exclusively including the articles published after 2014 to ensure the validity and reliability of the information., Results: A total of 53.33% of the articles reviewed focused on the United States, while 46.67% concentrated on Europe. The development of real estate in healthcare is essential in both regions due to the challenges faced with the current infrastructure. Study Limitation: Currently, there are very few studies concentrating on the research topic., Conclusions: The USA and Europe should focus on increasing real estate investments in healthcare by focusing on hospitals and trusts, rehabilitation centers, and nursing homes.

Background: Ventral hernia repairs (VHR) are frequent but loss- making. This study aims to identify epidemiological and procedure related factors in VHR and their influence on surgical training., Methods: Data from 86 consecutive patients who underwent VHR in 2019 was collected. Moreover, 66 primary ventral hernias and 20 incisional hernias were repaired in open procedures. Linear regression models were made., Results: Primary VHR procedures showed a mean deficit of -378.17 CHF per case. Incisional hernia repair procedures resulted in a deficit of -1442.50 CHF per case. The two hernia groups were heterogeneous. For the primary VHR procedures, the surgery time (β = 0.564, p < 0.001) had the greatest influence, followed by the costs of the mesh (β = -0.215, p < 0.001). The epidemiological factors gender (β = 0.143, p < 0.01) and body mass index (BMI) (β = -0.087, p = 0.074) were also influential. For incisional hernia procedures a surgeon's experience had the most significant influence (β = 0.942, p < 0.001), and the second largest influence was the price of the mesh (β = -0.500, p < 0.001). The epidemiological factor BMI (β = -0.590, p < 0.001), gender (β = -0.113, p = 0.055) and age (β = -0.026, p < 0.050) also had a significant influence., Conclusion: Our analysis shows a way of improving financial results in the field of ventral hernia repair. Costs can be visualized and reduced to optimize revenue enhancement in surgical departments. In our analysis primary ventral hernias are an appropriate training operation, in which the experience of the surgeon has no significant impact on costs. In primary VHR procedures, revenue enhancement is limited when using an expensive mesh. However, the treatment of incisional hernias is recommended by specialists. The financial burden is significantly higher with less experience. Therefore, these operations are not suitable for surgical training. The re-operation rate decreases with increasing experience of the surgeon. This directly affects the Patient Related Outcome (PROM) and quality of treatment. Therefore, high-quality training must be enforced. Since financial pressure on hospitals is increasing further, it is crucial to investigate cost influencing factors. The majority of Swiss public hospitals will no longer be able to operate ventral hernias profitably without new concepts. In addition to purchasing management, new construction projects, and mergers, improving the results of individual departments is a key factor in maintaining the profitability of hospitals in the future regarding hernia repair without losing the scope of teaching procedures.

Oncostatin M is a polypeptide cytokine, produced by normal and malignant hematopoietic cells, that has several in vitro activities, including the ability to inhibit growth of cultured carcinoma cells. Here we present a structural and functional comparison of two oncostatin M-related proteins (Mr 36,000 and 32,000) secreted by COS cells transfected with oncostatin M cDNA. The smaller of these forms lacked a hydrophilic C-terminal domain comprising predominantly basic amino acids. This domain was also absent from native oncostatin M produced by U937 cells. The 32,000-Mr form of oncostatin M was not produced by cells transfected with plasmids (G195 and G196) in which a potential trypsinlike cleavage site within the hydrophilic C-terminal domain was altered by site-directed mutagenesis. A 32,000-Mr fragment was produced by trypsin treatment of the 36,000-Mr form of oncostatin M. These observations suggest that the 32,000-Mr form of oncostatin M was derived from the 227-amino-acid propeptide by proteolytic cleavage at or near the paired basic residues at positions 195 and 196. Pro-oncostatin M was equally active in radioreceptor assays as the processed form but was 5- to 60-fold less active in growth inhibition assays. Likewise, nonprocessed mutant protein encoded by plasmid G196 was equally active in the radioreceptor assays as the processed form but was five- to ninefold less active in growth inhibition assays. Thus, the highly charged C-terminal domain of pro-oncostatin M is not required for receptor binding or growth-inhibitory activity but may alter the functional properties of the molecule. Propeptide processing of oncostatin M may be important for regulating in vivo activities of this cytokine.

Oncostatin M is a novel growth regulator originally isolated from differentiated human histiocytic lymphoma cells and activated T-lymphocytes based on its ability to inhibit the growth of A375 melanoma cells. We report here that oncostatin M is a widely acting regulator which alters the growth and/or morphology of cells derived from a variety of cancer cell types. At picomolar concentrations, recombinant oncostatin M inhibited the growth of 13/24 tumor cell lines. Six out of 7 lung cancer cell lines were inhibited by oncostatin M, but none of 6 colon cancer cell lines were affected. Oncostatin M also stimulated the growth of some normal cells (3/6), indicating that it, like many growth regulators, is bifunctional. Oncostatin M receptors appear necessary but not sufficient for a growth response to oncostatin M, since none of the cell lines lacking receptor responded to oncostatin M, whereas many but not all cell lines with receptor responded to oncostatin M. Receptor size (Mr congruent to 150,000) was similar for cells in which growth was inhibited, stimulated, or unaffected by oncostatin M.

Oncostatin M is a polypeptide of Mr approximately 28,000 that acts as a growth regulator for many cultured mammalian cells. We report the cDNA and genomic cloning, sequence analysis, and functional expression in heterologous cells of oncostatin M. cDNA clones were isolated from mRNA of U937 cells that had been induced to differentiate into macrophagelike cells by treatment with phorbol 12-myristate 13-acetate, and a genomic clone was also isolated from human brain DNA. Sequence analysis of these clones established the 1,814-base-pair cDNA sequence as well as exon boundaries. This sequence predicted that oncostatin M is synthesized as a 252-amino-acid polypeptide, with a 25-residue hydrophobic sequence resembling a signal peptide at the N terminus. The predicted oncostatin M amino acid sequence shared no homology with other known proteins, but the sequence of the 3' noncoding region of the cDNA contained an A + T-rich stretch with sequence motifs found in the 3' untranslated regions of many cytokine and lymphokine cDNAs. Oncostatin M mRNA of approximately 2 kilobase pairs was detected in phorbol 12-myristate 13-acetate-treated U937 cells and in activated human T cells. Transfection of cDNA encoding the oncostatin M precursor into COS cells resulted in the secretion of proteins with the structural and functional properties of oncostatin M. The unique amino acid sequence, expression by lymphoid cells, and growth-regulatory activities of oncostatin M suggest that it is a novel cytokine.

A novel screening assay was used to test 13 previously described antibreast cancer antibodies for those which recognize antigens elevated in serum of breast cancer patients. Binding of three of these antibodies to breast or lung carcinoma cells was inhibited to a significantly greater extent by tumor patient serum than by normal serum, suggesting that the antigens might be useful serum markers. Two of these antibodies, W1 and W9, were shown to recognize nonoverlapping epitopes on a high molecular weight molecule(s) purified from serum from breast cancer patients. A sensitive double determinant immunoassay was developed to measure W1 antigen levels in sera from a total of 389 cancer patients and controls. Forty seven % (37 of 79) of individuals having breast cancer showed elevated serum levels of the W1 antigen, whereas only 4% (1 of 25) of normal controls and 2% (1 of 47) of patients hospitalized for nonmalignant disorders showed elevated levels. These differences were statistically significant (P less than 0.001). The percentage of breast cancer patients showing elevated serum levels was greater for individuals with metastatic disease. Statistically significant numbers of lung, ovarian, and prostate, but not colon, cancer patients also had elevated serum levels of the W1 antigen. These data suggest that measurement of the W1 antigen in serum might provide clinically useful information on the course of metastatic breast and other cancers.

Monoclonal antibodies with specificity for mucin-like antigens have shown great promise for the diagnosis and therapy of human cancer. Heterogeneity in the expression of mucin-like antigens by tumor and normal cells has been noted in several previous studies. An understanding of the nature of this heterogeneity has important implications for the diagnostic and therapeutic usefulness of antibodies to mucin-like antigens. We have studied the mechanism of variability in expression of epitopes on a mucin-like antigen defined by monoclonal antibodies W1, W5, and W9 in the lung carcinoma cell line, Calu-1. Using the fluorescence activated cell sorter and clonal analysis, we have demonstrated that intercellular variability in mucin antigen expression by Calu-1 cells can be explained in part by heritable variation in the tumor cell population. Clonal cell lines were isolated which differ greatly in levels of epitopes for all three mucin directed antibodies. Levels of all three epitopes showed significant variation between different clonal lines but in general were coordinately regulated. Differences in epitope expression between two lines studied in detail could be attributed to a dramatic difference in expression of a high molecular weight mucin-like glycoprotein. In immunoblotting experiments the binding of all three antibodies to this glycoprotein was affected by sodium periodate and/or neuraminidase treatment, suggesting that the antibodies recognize carbohydrate epitopes. Thus, heterogeneity in expression of mucin-like glycoprotein antigens can result from heritable variations which affect expression of multiple carbohydrate epitopes.

The murine anti-human lung tumor monoclonal antibody L3 recognizes antigens found both in the medium of cultured carcinoma cells and in normal human serum. Sequential immunoprecipitation experiments indicate that the L3 antigen is also recognized by a previously described monoclonal antibody directed against a melanoma-associated antigen [Natali, P. G., Wilson, B. S., Imai, K., Bigotti, A., & Ferrone, S. (1982) Cancer Res. 42, 583-589]. This antibody precipitated a Mr 76000 glycoprotein from metabolically labeled extracts of the lung carcinoma cell line Calu-1 and a Mr 94 000 glycoprotein from labeled culture medium. Pulse-chase experiments suggested a precursor-product relationship between these molecules. Analysis of glycosidase sensitivities of the two forms indicated that maturation of carbohydrate side chains correlated with the apparent increase in molecular weights. L3 antigenic activity, measured in a competitive radiometric cell binding assay, was purified more than 90-fold from serum-free medium of Calu-1 cells and more than 3000-fold from normal human serum. The major immunoreactive components purified from culture medium and serum were identical with respect to apparent molecular weight, electrophoretic mobility, pI, glycosidase sensitivity, and V8 protease fingerprints. In addition, the sequence of the amino-terminal 16 N-terminal amino acid residues of the major immunoreactive species from both sources was identical. The properties of the L3 antigen did not correspond to those of any known protein, suggesting that this serum protein has not been previously characterized.

Oncostatin M is a polypeptide growth regulator produced by activated T cells and phorbol ester-treated U937 cells. To identify specific cellular receptors for this factor, we have characterized the binding of 125I-labeled oncostatin M to a variety of normal and malignant mammalian cells. Recombinant oncostatin M was labeled with 125I with full retention of growth inhibitory activity on A375 melanoma cells. 125I-Oncostatin M bound to sensitive cells in a time- and temperature-dependent fashion. Binding was specifically inhibited by unlabeled native or recombinant oncostatin M, but not by other polypeptide growth factors tested. Binding to human leukemic and normal blood cells was generally less than to nonhematopoietic cells. With four different cell lines, maximal growth inhibition by oncostatin M was achieved at less than maximal binding site occupancy. Scatchard graphs of direct binding data were curvilinear and indicated that 125I-oncostatin M bound with higher apparent affinity at lower 125I-oncostatin M concentrations. Using a two binding site model, affinity constants of Kd1 = 11 +/- 11 pM and Kd2 = 1000 +/- 380 pM were extrapolated from binding data with A375 cells, and values of Kd1 = 3 +/- 2 pM and Kd2 = 400 +/- 44 pM from A549 cells. The major 125I-oncostatin M binding species in a number of mammalian cell lines was identified by chemical cross-linking as a specific protein(s) of Mr = 150,000-160,000. 125I-Oncostatin M was internalized (t1/2 = 30 min) and degraded subsequent to binding to a responsive cell line.(ABSTRACT TRUNCATED AT 250 WORDS)