The epidural anesthesia on the general anesthetic (GA) requirements has not been well investigated. Therefore, we conducted the present study to explore the predicted effect-site concentration of propofol  vs. 3.36 (95% CI: 3.19–3.53) μg mL−1, (p = 0.036)]. The consumption of anesthetics was lower in the GA + EA group than in the GA alone group . The emergence time was shorter in the GA + EA group than in the GA alone group .The EC50 of predicted Ceprop for LOC, decreased the consumptions of propofol and remifentanil during maintenance of anesthesia, and fastened recovery from anesthesia.Concomitant epidural anesthesia reduced by 15% the ECClinicalTrials.gov, identifier: NCT05124704. Combined epidural-general anesthesia (GA + EA) has been well accepted to be the preferred anesthetic technique for major upper abdominal and thoracic surgeries  due to bWe hypothesized that the effect-site concentration of propofol required for loss of consciousness (LOC) could be decreased by concomitant epidural anesthesia during the induction of general anesthesia.50) of propofol for inducing LOC in patients undergoing open gastrectomy under combined epidural-general anesthesia and general anesthesia alone using the up–down sequential allocation method. We also investigated and compared the consumption of anesthetics, hemodynamic status, emergence time, and postoperative outcomes between combined epidural-general anesthesia and general anesthesia alone.Therefore, we conducted this prospective, randomized, and double-blind trial to investigate the median (50%) effective effect-site concentration . We followed the consolidated Standards of Reporting Trials (CONSORT) statement when conducting and reporting this trial. This study was conducted between 20 November 2021 and 25 May 2022 at the Department of Anesthesiology, Zhejiang Cancer Hospital.This study was approved by the Ethics Committee of Zhejiang Cancer Hospital prior to patient enrolment  and registered at After obtaining written informed consent, 60 ASA physical states II or III patients with gastric cancer aged 18–75 years scheduled for open gastrectomy were enrolled in this study. The exclusion criteria were as follows: contraindications to epidural puncture or catheter placement; chronic or acute (within 48 h) intake of psychotropic drugs, benzodiazepines, anticonvulsants, or opioids; alcoholism; hepatic, renal, neurological, or other organ dysfunction; allergy to any drugs used in this study; or refusal to receive epidural puncture.The eligible patients were randomized to one of the two groups with different anesthesia protocols: the GA + EA group  or the GA alone group . Randomization was performed by a research assistant who did not take part in the study using computer-generated numbers . The randomization codes were concealed in numbered, sealed opaque envelopes, one of which was opened for each patient.2), invasive artery blood pressure (IBP) via the left radial artery, bispectral index (BIS), and end-tidal carbon dioxide (EtCO2) was applied. A central venous catheter was placed in the right internal jugular vein and commenced an infusion of 37°C Ringer’s lactate solution at a rate of 10 mL kg−1 h−1.No premedication was given. Upon arrival in the operating theater, standard monitoring including electrocardiogram (ECG), pulse oximetry  in a 20 mL syringe. A test dose of 3 mL of the epidural medication  was given to exclude an intrathecal placement of the epidural catheter. After 4 min, in the absence of significant sensory or motor blockade, the patient received an extra 5 to 8 mL (depending on the height and weight) of the epidural medication  through the epidural catheter. The level of sensory blockade was assessed bilaterally in the anterior axillary line by pinprick 15 min after epidural injection. The upper and lower limits of the block level were recorded. If the upper block level reached T4 or above, but did not exceed T3, the epidural infusion was then maintained with 0.375% ropivacaine at an infusion rate of 4–6 mL hGeneral anesthesia was induced with propofol oxycodone and rocuronium by the fixed attending anesthesiologists  who were blinded to the patient grouping and epidural administration. Propofol was administered via a target-controlled infusion (TCI)  with the pharmacokinetic model of Schnider et al. .50 of propofol-inducing LOC  in patients with or without prior epidural anesthesia, the administration of propofol for each patient was applied according to that applied in our previous study  somatic arousal: signs of coughing, chewing, and grimacing; and (4) somatic response: purposeful movement. The maximum infusion rate was 0.25 μg kg−1 min−1. Hypotension, defined as a MAP <80% of baseline or <60 mmHg, was treated initially by speeding intravenous infusion and decreasing the remifentanil infusion rate by 0.05 μg kg−1 min−1 in a stepwise manner until the minimum rate of 0.05 μg kg−1 min−1, and finally, ephedrine, phenylephrine, or metaraminol were given. Bradycardia was treated with intravenous atropine 0.5 mg.Anesthesia was maintained with propofol and remifentanil. Cewareness . In the  follows : (1) hyp−1 sufentanil was applied for postoperative pain for all patients. In the postanesthesia care unit (PACU), the modified Aldrete-Score, postoperative nausea and vomiting, pain intensity , and other side effects were recorded. All patients were visited on the 1st day postoperatively to check for adverse effects of anesthesia, and patient satisfaction with the anesthesia procedure was assessed using a scale of 0–10 rating (0 = completely dissatisfied and 10 = completely satisfied) and the quality of postoperative analgesia with PCEA using the variables such as pain VAS and the number of total PCE presses and effective presses.Both propofol and remifentanil infusions were discontinued when the final surgical suture was completed. Emergence time from anesthesia was assessed by measuring the duration between the time of discontinuation of anesthetics and the time of spontaneous opening of eyes. Patient-controlled epidural analgesia (PCEA) with 0.175% ropivacaine mixed with 0.7 μg mL50 calculated by using the modified Dixon up–down method for most realistic scenarios , and intergroup comparisons were performed using the Mann–Whitney U-test. Categorical variables were presented as number (%) and were analyzed using the chi-square test.Continuous variables were tested for normality using the D’Agostino and Pearson tests. Variables with normal distribution were presented as the mean ± standard deviation (SD), and intergroup comparisons were performed using student’s 50 and EC95 values for EC50 of propofol were determined by calculating the mean of the midpoints of pairs of Ceprop administered in successive patients in which a positive response was followed by a negative response or a negative response was followed by a positive response (turning points) according to the modified up-and-down allocation method as described previously. At least six pairs of negative–positive responses were needed in each group for the final analysis , SPSS version 22.0 , and R package version 0.1.1 were used for statistical analysis. A n = 30 each) and included in the final analysis. Baseline demographic and surgical characteristics were comparable between groups  flow diagram is shown in n groups .prop are shown in 50 of propofol for LOC, determined using Dixon’s up–down method, was lower in the GA + EA group [2.97 (95% CI: 2.63–3.31) μg mL−1] compared with the GA alone group [3.36 (95% CI: 3.19–3.53) μg mL−1] (p = 0.036). The EC50 and EC95 values of propofol calculated using probit regression analysis were 2.71 (95% CI: 2.32–3.04) μg mL−1 and 3.71 (95% CI: 3.31–4.86) μg mL−1, respectively, in the GA + EA group, and 3.19 (95% CI: 2.83–3.54) μg mL−1 and 4.19 (95% CI: 3.76–5.42) μg mL−1, respectively, in the GA alone group. The relative mean potency for propofol-inducing LOC in the GA alone group versus the GA + EA group was 0.85. In addition, the predicted Ceprop at the time of LOC was significantly lower in the GA + EA group, 1.97 (95% CI: 1.75–2.16) μg mL−1, compared with the GA alone group, 2.63 (95% CI: 2.49–2.76) μg mL−1 (p < 0.001).Individual responses to propofol at corresponding Ce−1 min−1 vs. 0.13 ± 0.02 mg kg−1 min−1, p = 0.014;) (remifentanil: 0.08 ± 0.03 μg kg−1 min−1 vs. 0.14 ± 0.04 μg kg−1 min−1p < 0.001).Both propofol and remifentanil consumptions  were significantly lower in the GA + EA group compared with the GA alone group (propofol: 0.11 ± 0.02 mg kgp = 0.013). The cumulative percentages of patients remaining unconscious after discontinuation of anesthetics infusion for both groups are shown in The emergence time from anesthesia was significantly shorter in the GA + EA group [16.0 (IQR: 11.0–19.3) min] compared with the GA alone group [20.5 (IQR: 14.5–25.3) min] using the Kaplan–Meier log-rank survival analysis (p = 0.29). The pain score in the PACU was lower in the GA + EA group than that in the GA alone group (p < 0.001). The total numbers and the effective numbers of PCEA press on the 1st day postoperatively were lower in the GA + EA group than in the GA alone group . The incidence of postoperative side effects, such as hypotension, nausea, and vomiting, and also patient satisfaction score and time to first flatus were comparable between groups   .prop) required for achieving loss of consciousness (LOC) in 50% of patients (EC50) was significantly lower in patients with prior thoracic epidural anesthesia (TEA) with 0.375% ropivacaine (GA + EA group) than in patients without prior TEA . The magnitude effect of TEA, the difference between the calculated EC50 of propofol in two groups, was 15% in the probit regression method and 12% in the up–down method. In addition, we also demonstrated that the patients in the GA + EA group had lower consumptions of both propofol and remifentanil during maintenance of anesthesia, shorter emergence time from anesthesia, and better postoperative pain control compared with the patients in the GA alone group.In this prospective, randomized, double-blind study, using the up–down sequential method, we mainly demonstrated that the predicted effect-site concentration of propofol  and detect the EC95 of propofol for LOC. Considering the fact that the administration of propofol using the TCI technique is very popular, the results of predicted Ceprop required for LOC found in the present study could be a reference for clinical practice. Consequently, we assessed the sensory block level 15 min after epidural infusion. This time may not be long enough for the local anesthetics to cease its maximal rostral spread after epidural infusion; thus, the actual sensory level during induction of anesthesia might be higher than we recorded. Next, epidural anesthesia is an invasive procedure that may have some risks and side effects such as low blood pressure, back pain, sympathetic nerve blockade, damage to nerves at the injection site, discitis, osteomyelitis, or meningitis. Finally, we measured the BIS value but did not record and analyze this variable. Further studies are needed to be conducted to minimize these limitations.Our study has several limitations. First, we did not measure the plasma concentrations of propofol  can be found at: The studies involving humans were approved by Ethics Committee of Zhejiang Cancer Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.JW and XCh: study design, data analysis, and manuscript preparation. JW, YS, WG, XCu, WZ, and SC: conducting the study. WG and WZ: data collection. XCh: random code generation. All authors contributed to the article and approved the submitted version.


An 89‐year‐old patient with gallbladder neuroendocrine neoplasms (GB‐NENs) and lung cancer metastasis underwent nivolumab monotherapy, resulting in tumor shrinkage. Surgery and adjuvant nivolumab showed efficiency despite low expression of PD‐L1. During the follow‐up PET‐CT for pulmonary adenocarcinoma, accumulation was detected around the gallbladder. EUS‐FNA confirmed the presence of small‐cell neuroendocrine carcinomas (SCNECs). Chemotherapy proved intolerable, leading to the initiation of nivolumab monotherapy, resulting in tumor shrinkage. Subsequent surgery and adjuvant nivolumab yielded positive outcomes. The postoperative pathology diagnosed the gallbladder tumor as NECs, with an extremely low PD‐L1 expression. Nivolumab may be effective in GB‐NEN despite low PD‐L1 expression. He had no disability nor any signs of cognitive dysfunction.He had been suffering from left upper lobe S1+2) lung cancer . Left upper lobectomy with regional and left mediastinal lymphadenectomy had been performed 3 years prior, but was followed by mediastinal lymph node metastases and an intrapulmonary metastasis in the right lobe 2 years before. The patient received chemoradiotherapy which stabilized the metastases. The patient's medical comorbidities included left internal carotid artery stenosis, early colorectal cancer, early bladder cancer, hypertension, and hyperuricemia. The patient had no abdominal symptoms or physical examination findings, but follow‐up PET‐CT revealed that+2 lung c3As a result of the examination, the patient was diagnosed with primary GB cancer and metastatic lung cancer of the right lower lobe (S6) . Endoscopic ultrasound‐guided fine needle aspiration (EUS‐FNA) of lymph nodes in the hepatoduodenal ligament revealed small‐cell neuroendocrine carcinomas (SCNECs), which indicated primary GB cancer with lymph node metastasis, rather than metastases of the lung adenocarcinoma.Generally, prognosis of GB carcinoma with lymph node metastasis and SCNEC is quite poor.Furthermore, surgery seemed likely to be tolerated by the patient because he was fairly healthy and because the surgery did not require major hepatectomy or bile duct resection. We carefully explained the foregoing to the patient and his family and obtained consent for surgery. Then, an extended cholecystectomy and lymphadenectomy around the hepatoduodenal ligament and sampling of lymph nodes proximal to the abdominal aorta were performed.4After surgery, no major adverse events were observed, except delayed gastric emptying, Clavien dindo grade II, and acute gastric mucosal lesions, Clavien dindo grade IIIa. Histopathologically, the atrophied gallbladder had a tumor in its fundus (15 × 8 × 10 mm). Figure . Upon mi5Primary GB‐NENs account for 0.5% of all NENs and 2.1% of all GB cancers.In the present case, considering the patient's age and medical history, we started nivolumab monotherapy. As a result of image examination, though the paraaortic lymph node swelled, the GB tumor clearly shrank, and swelling of hepatic duct lymph node disappeared. A tumor marker against NEC, NSE turned negative.These findings suggest that nivolumab can be effective against GB‐NEC. Nevertheless, PD‐L1 28‐8 immunohistochemistry (IHC) was positive in less than 10% of cancer cells. This suggests the following two possibilities: (1) PD‐L1 positive cells were killed by nivolumab, leaving only colonies of negative cells. (2) Nivolumab activates T cells, which kill PD‐L1 negative cells. Drug sensitivity of cancer cells varies from cell to cell because each cancer cell has a different genetic background. Therefore, although ICIs are very effective for cancer cells with high PD‐L1, ICIs may also be effective against subgroups with low or undetectable PD‐L1. Actually, it has been reported that in lung cancer, ICIs respond even when PD‐L1 expression is extremely low.EUS‐FNA is quite useful because GB cancer sensitivity is 96%.It is necessary to choose a chemotherapy regimen that is effective for all tumor components. Multiple EUS‐FNA enables collection of multiple samples, which can facilitate correct diagnosis and selection of an appropriate chemotherapy regimen, but dissemination and bile leakage are problematic.In conclusion, EUS‐FNA is useful for diagnosis of GB‐NECs. However, because the primary tumor may be MiNEN rather than NEC, it is better to perform a biopsy when selecting a chemotherapy regimen. Nivolumab may enhance the immune function of T cells by some means other than inhibition of PD‐1, and it may be effective against GB‐cancers involving NEC despite the low expression of PD‐L1.Shinichiro Kanda: Writing – original draft. Kazuhiro Hiyama: Supervision. Izumi Kirino: Supervision. Yasuo Fukui: Supervision. Hideo Terashima: Supervision.None.The authors declare that they have no conflicts of interest and that there are no relevant financial disclosures to report.Written, informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review upon request.


Obesity may be associated with increased risk of recurrence and progression in patients with non‐muscle invasive bladder cancer (NMIBC), but evidence is limited and inconsistent. We examined the associations of body mass index (BMI), waist circumference, and waist‐to‐hip ratio (WHR) with risk of recurrence and progression among patients with NMIBC.This prospective study included 1029 patients diagnosed with primary NMIBC between 2014 and 2017. Patients reported weight 2 years before diagnosis at baseline, and weight, waist and hip circumference at 3 months postdiagnosis. Associations were quantified using Cox proportional hazard analyses, adjusted for clinical and lifestyle characteristics.per 5 kg/m2 0.94; 95% CI 0.82, 1.07), waist circumference , or WHR . In contrast, higher BMI was associated with a 40% increased risk of progression, with only the 2‐year prediagnosis association reaching statistical significance . No associations for pre‐to‐postdiagnosis weight change were found.More than half of patients were overweight (49%) or obese (19%) after diagnosis. During a median follow‐up time of 3.6 years, 371 patients developed ≥1 recurrence and 53 experienced progression. No associations with recurrence were observed for BMI (HRGeneral and abdominal obesity were not associated with recurrence risk among patients with NMIBC, but might be associated with increased risk of progression. Studies with sufficient sample size to stratify by tumor stage and treatment are needed to better understand whether and how obesity could influence prognosis. Seven cohort studies showed that higher body mass index (BMI) was associated with higher risk of recurrence,To date, studies have not focused on other measures of obesity than BMI and have not included changes in weight over time. Using data of a prospective cohort study, we examined the associations between several measures of obesity  with the risk of recurrence and progression in patients with NMIBC, while adjusting for smoking and other lifestyle factors. Furthermore, we examined whether pre‐to‐post diagnosis weight change was associated with NMIBC recurrence and progression.22.1We used data from the UroLife study, a prospective multicenter cohort study among patients diagnosed with primary NMIBC.2.22). Patients with BMI <18.5 kg/m2 (n = 10) were excluded from the analyses , we used BMI assessed at baseline (correlation between both measures of BMI was 0.99). Pre‐to‐postdiagnosis weight change was calculated as the change in weight between 2 years prediagnosis and 3 months postdiagnosis and expressed as percentage weight change. Weight change was defined as weight loss ≥5%, weight stable −5% to 5%, and weight gain ≥5%, as ≥5% weight loss has been proven to have significant health benefits and is generally accepted as criterion for clinically meaningful weight change.Information on anthropometrics was collected at 2 time points via self‐reported web‐based or paper‐and‐pencil based questionnaires. First, at approximately 6 weeks after diagnosis (baseline), information on current height (and body weight) and body weight 2 years before diagnosis was collected. Second, at 3 months after diagnosis information on current body weight, waist circumference, and hip circumference was collected. Waist (midway between the lowest rib and the iliac crest) and hip (widest part) circumference were measured in centimeters with a tape sent to participants. Anthropometric measures were used as continuous and predefined categorical variables, that is, obesity (BMI ≥30.0), overweight (BMI ≥25‐ < 30), and healthy weight  in case of Ta and T1 tumors and by the date of the first tumor‐negative cystoscopy after primary TURBT in case of (concomitant) carcinoma in situ (CIS). Progression was defined as the first occurrence of stage or grade progression.2.4At baseline, patients reported sociodemographic information and whether they were ever diagnosed with diabetes mellitus. Smoking status was reported at baseline (reflecting pre‐diagnosis smoking) and 3 months postdiagnosis. Physical activity was assessed using the validated SQUASH questionnaire.Clinical data, such as tumor stage, differentiation grade, presence of (concomitant) CIS, tumor focality, and treatment, was collected from medical records by trained data managers of the Netherlands Cancer Registry. Participants were divided into low‐, intermediate‐ and high‐risk groups according to the European Association of Urology (EAU) guidelines 2019 based on stage, grade, CIS, and focality,2.5First recurrence was the primary endpoint; progression was a secondary endpoint. Cox proportional hazard regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of anthropometry measures with recurrence and progression. Follow‐up time began at baseline or follow‐up questionnaire completion (progression) or at the first day of being tumor‐free in case a person was not tumor‐free at questionnaire completion (recurrence). In addition, we analyzed multiple recurrences with an extended Cox model for recurrent event data (gap time—unrestricted (GT‐UR) model with a common baseline hazard and random effect).Subgroup analyses were performed to explore whether the associations between BMI at 3 months postdiagnosis and recurrence were modified by age at diagnosis, sex, smoking status, diabetes, EAU risk group, tumor stage, tumor grade, and initial treatment.p‐values ≤0.05 were considered statistically significant.All analyses were performed in R for Windows version 4.1.3 . 3Of the 2133 invited patients, 2025 patients were eligible and 1076 (53%) agreed to participate . Pearson correlation coefficients of BMI 3 months postdiagnosis with waist circumference, WHR, and BMI 2‐year prediagnosis were 0.79, 0.38, and 0.89, respectively.Baseline characteristics for the total study population and according to BMI 3 months postdiagnosis are listed in Table During a median (IQR) follow‐up time of 3.6 (1.5–4.4) years, 371 patients (36%) experienced at least one tumor recurrence. Only 53 patients (5%) experienced progression during a median (IQR) follow‐up time of 4.1 (3.6–4.8) years.3.12 increase in BMI. Results were similar for BMI 2 years prediagnosis . There was little evidence of confounding by clinical and treatment characteristics, smoking status, and other lifestyle factors compared with the model only accounting for age at diagnosis and sex   Table . The HR 3.4per 5% 1.02; 95% CI: 0.93, 1.13) nor progression  (Table Weight change from 2 years prediagnosis to 3 months postdiagnosis was neither associated with risk of recurrence (HR 3) Table . [Correc4In this prospective study among 1029 patients diagnosed with primary NMIBC, BMI, waist circumference, and WHR were not associated with risk of recurrence. In contrast, higher BMI tended to be associated with an increased risk of progression. Weight change from 2‐years prediagnosis to 3 months postdiagnosis was neither associated with recurrence nor progression.4.1In contrast to our results, seven out of nine previous observational studies in patients with NMIBC showed that higher BMI was associated with an increased risk of recurrence.In line with our results, a meta‐analysis of three cohort studies showed that obesity, compared to healthy weight, was associated with an increased risk of progression in patients with NMIBC.This was the first study that examined the association between weight change and NMIBC prognosis. We found that weight change from 2 years prediagnosis to 3 months postdiagnosis was neither associated with risk of recurrence nor with progression. However, it should be noted that only 28% of participants experienced a weight change ≥5%. For cancer patients, it is important to know whether changing one's lifestyle after diagnosis can lower the risk of recurrence and progression. Additional studies are needed to further examine whether obesity after NMIBC diagnosis and changes therein can impact prognosis.4.2Our study had several limitations. First, anthropometrics were self‐reported at each time point. Generally, errors are present in self‐reported anthropometric data; some people underreport, while others overreport and the magnitude of measurement error in waist circumference is unclear.Strengths of our study compared with previous studies are the inclusion of multiple measures of obesity and adjustment of our analyses for smoking status and other lifestyle factors. In contrast to previous studies, our study assessed the effects of both general and abdominal obesity. We observed similar associations with NMIBC outcomes for both general and abdominal obesity. Abdominal obesity, measured by waist circumference or WHR, is associated with adverse metabolic profiles such as insulin resistance and systemic inflammation, which are both linked to tumor growth.5In conclusion, general and abdominal obesity were not associated with an increased risk of recurrence among patients with NMIBC but tended to increase risk of progression. As current evidence is inconsistent, further studies with obesity measures, preferably also including CT‐based visceral and subcutaneous fat, and with sufficient sample size to stratify by tumor stage and type of treatment are needed to understand whether and how obesity after diagnosis could influence risk of recurrence and progression.Moniek van Zutphen: Conceptualization ; data curation ; formal analysis (lead); methodology ; visualization (lead); writing – original draft (lead); writing – review and editing (lead). Ivy Beeren: Data curation ; investigation (lead); writing – review and editing (supporting). Katja KH Aben: Resources ; writing – review and editing (supporting). Antoine G van der Heijden: Resources ; writing – review and editing . J. Alfred Witjes: Resources ; writing – review and editing (supporting). Lambertus A.L.M. Kiemeney: Conceptualization ; writing – review and editing (supporting). Alina Vrieling: Conceptualization ; funding acquisition (lead); methodology ; project administration (lead); supervision (lead); writing – review and editing .This work was supported by Alpe d'HuZes/Dutch Cancer Society (KUN 2013–5926), Dutch Cancer Society (2017–2/11179), and World Cancer Research Fund International and Wereld Kanker Onderzoek Fonds (IIG_2019_1957). The funding sources were not involved in study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, and the decision to submit the article for publication.The authors declare no conflict of interest.This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval was provided by the Committee for Human Research region Arnhem‐Nijmegen (CMO 2013–494). Informed consent was obtained from all individual participants included in the study.Supplementary Table 1.Click here for additional data file.


Adults aged 65 years and above account for over half of all cancer diagnoses in the United States, but little is known about trend of elderly cancer survival in the United States. We aimed to assess the survival trend for elderly cancer in the United States from 1995 to 2014.We used data from Surveillance, Epidemiology, and End Results 12 registries and included 1,112,441 eligible patients aged 65 years or older who were diagnosed between 1995 and 2014 with cancer and followed up until December 2019. Overall and stage‐specific 5‐year relative survival, ratio of observed survival to expected survival, with 95% confidence intervals (CIs) of elderly cancer patients stratified by age were estimated during four periods . Cox proportional hazards models were used to estimate hazard ratios for cancer‐specific death among patients diagnosed during 2000–2004, 2005–2009, 2010–2014, compared diagnoses in 1995–1999. We also calculated stage distribution and treatment rate during four periods.In the United States, 5‐year relative survival for elderly cancer patients improved from 57.3% (95% CIs 57.0–57.5) in 1995–1999 to 60.7% (60.5–60.9) in 2010–2014. After controlling for sociodemographic and tumor characteristics, about a 19% reduction in cancer‐specific deaths among diagnoses in 2010–2014 compared with 1995–1999. Cancer survival improved for elderly patients in all age groups, with exception of stable survival for patients aged 85 and above. Comparing 1995–1999 with 2010–2014, relative survival improved from 84.7% (84.3–85.1) to 86.7% (86.3–87.0) for localized stage and from 12.4% (12.1–12.7) to 18.7% (18.4–19.0) for distant stage for all cancers combined. The trends in stage distribution and treatment rate for all cancers combined were relatively stable.In the United States, survival for elderly cancer patients has improved slightly from 1995 to 2014, possibly mainly due to advances in treatment. Further studies are warranted to explore interventions to improve elderly cancer survival.  Relative survival, the ratio of cancer patients' observed survival to the expected survival of a comparable group from the general population, accounts for changes in background mortality over time  and is therefore focused on survival from cancer.22.1The data was obtained from the US National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 12 registries research database.We identified elderly patients (65 years or over) with first primary diagnoses of cancer using the third edition of the International Classification of Disease for Oncology for all cancers and 16 major cancer types, including acute leukemia, bladder, chronic leukemia, colon/rectum, corpus and uterus (“uterus”), esophagus, female breast, kidney, liver/intrahepatic bile duct (“liver”), lung, melanoma of skin, non‐Hodgkin lymphoma (“NHL”), ovary, pancreas, prostate, and stomach. The 16 cancer types accounts for around 85% of all elderly cancer cases. We chose patients diagnosed from 1995 to 2014 and followed up until December 31, 2019, with a minimum follow‐up period of 5 years. Patients aged less than 65 years, patients diagnosed by death certificate or autopsy, patients with unknown status, and patients with in situ tumors were excluded. Covariates included demographic variables  and diagnostic information . Age at diagnosis was divided into three groups . Race categories included white, black, and other . Stage referred as SEER historic stage, grouping patients into localized, regional, distant, and unknown stage. Urban area was defined as counties in metropolitan areas, while rural area was defined as nonmetropolitan areas.To compare survival in the United States with other countries, we extracted cancer survival from the IARC website based on the International Cancer Benchmarking Partnership (ICBP) SURVMARK project, which contains survival estimates based on high‐quality data from 21 population‐based cancer registries in seven high‐income countries .2.2Relative survival was defined as the ratio of cancer patients' observed survival to the expected survival of a comparable group from the general population. We calculated five‐year relative survival with corresponding 95% confidence intervals (CIs) of elderly cancer patients by using SEER*Stat software program (version 8.4.0).p < 0.001 as statistically significant.Cancer‐specific death was defined as death from specific cancer in the SEER registries. Cox proportional hazards models were used to calculate hazard ratios (HRs) and corresponding 95% CIs for cancer‐specific death associated with age for patients diagnosed during the periods 2000 to 2004, 2005 to 2009, and 2010 to 2014, and were compared with those diagnosed during the baseline period (1995 to 1999). Survival time was defined as the time from the date of diagnosis until cancer‐specific death, date of last contact or end of study period  in months. Cases were censored if patients still alive on December 31, 2019, lost to follow‐up, or died of other causes. To assess the average cancer‐specific mortality from 1995 to 2014, we estimated HRs and 95% CIs for each 5‐year increment by year of diagnosis. Potential confounders, including sex, race, age at diagnosis, and rural–urban area, tumor grade, marital status, common histology, and stage at diagnosis, were included in Cox model. Visual examination of Kaplan–Meier curves for each variable in the study revealed no violation of the proportional hazards assumption. Considering the large sample size and numerous tests performed in our study, we adopted two‐sided In addition, we examined the distribution of stage at diagnosis from SEER database and treatment rate stratified by the four calendar periods. As a sensitivity analysis, we analyzed the stage distribution of cancer patients with unknown stage, treating the unknown stage group as a separate group. The treatment variable was defined as the acceptance of any type of treatment including surgery, chemotherapy or radiation, during the first course of the therapy. Because the treatment variable had been collected since 1998, the period of treatment was divided into 1998–1999, 2000–2004, 2005–2009, and 2010–2014. We excluded non‐Hodgkin lymphoma and leukemia from the calculation of the proportion of stage. Data analysis was performed using SAS version 9.4 . We performed figures using R software version 4.1.2 .3We identified a total of 1,112,441 eligible patients aged 65 years and above who had been diagnosed with cancer in the 12 SEER registries from 1995 through 2014. The number of elderly cancer patients overall, by age group and site are shown in Table The 5‐year relative survival for elderly cancer patients varied considerably by cancer site, ranging from 7.1% (95% CI 6.6–7.9) for pancreatic cancer to 97.0% (96.6–97.4) for prostate cancer in 2010–14  in 65–74 years, 55.6% (55.2–56.0) in 75–84 years, and 41.3% (40.6–42.1) in 85‐years] in 2010–14 Figure .Survival gap persists between different stages for elderly cancer patients Figure . CompariFrom 1995 to 2014, the distribution of stages for all cancer combined was relatively stable. The proportion of localized cancer cases increased markedly for kidney, stomach, and liver cancer, but decreased for esophageal cancer Figure . Subgrou4Based on the most recent SEER data from population‐based cancer registries, our study evaluated the survival trend of elderly cancer patients in the United States between 1995 and 2014 and explore the potential causes of such trend. We found that the survival among elderly cancer patients in the United States has continued to increase over the past 20 years, except for patients aged 85 years and above. Given the significant increase in stage‐specific survival, stable treatment rate, and stable distribution of stage, the most likely reason for the improvement in elderly cancer survival is the progress in treatment. These findings could provide a scientific basis for facilitating policy designed to improve cancer survival in the elderly.Survival for elderly cancer patients improved slightly in the United States from 1995 to 2014. Stage at diagnosis is an important factor that affects the prognosis of cancer patient.Treatment is another key prognostic factor.Unsurprisingly, we observed a survival gap by age among elderly cancer patients. This is consistent with our findings that older patients are more likely to be diagnosed at a later stage and receive less treatment, which may account for the survival gap. Meanwhile, we found that the percentage of patients in localized stage increased among those aged 65–74 years, but remained stable even decreased among those aged 75–84 years and aged 85 or older. This might be associated with less screening among older patients.Compared with other high‐income countries with comparable health system expenditure, and universal healthcare access,This study has several limitations. First, the SEER 12 registries only cover about 12.2% of the US population, so its generalizability is unclear. We were also unable to assess various factors that may influence survival, such as frequency of comorbidities, postoperative complications, and emergency presentation. What is more, potential lead‐time bias could be an issue in this study, but we could not eliminate the influence of this bias on the results.In summary, this study assesses and quantifies the degree of trend in survival for elderly cancer patients in the United States from 1995 to 2014. We observe a slight improvement in the survival of elderly cancer patients in the United States over time, except for those aged 85 years and up. Our results may support that the improvement in elderly cancer survival are more likely related to the progress in cancer treatment in the United States It emphasizes the critical need to promote the age‐friendly healthcare systems and the creation of high‐quality care teams for elderly cancer patients. Future population‐based studies are warrant to explore interventions to improve the elderly cancer survival.Lan An: Conceptualization ; formal analysis ; methodology ; visualization ; writing – original draft . Ju Wen: Data curation ; supervision ; validation ; writing – review and editing . Rongshou Zheng: Data curation ; supervision ; validation ; writing – review and editing . Hongmei Zeng: Data curation ; supervision ; validation ; writing – review and editing . Siwei Zhang: Data curation ; supervision ; validation ; writing – review and editing . Ru Chen: Data curation ; supervision ; validation ; writing – review and editing . Kexin Sun: Data curation ; supervision ; validation ; writing – review and editing . Li Li: Data curation ; supervision ; validation ; writing – review and editing . ShaoMing Wang: Conceptualization ; funding acquisition ; supervision ; validation ; writing – review and editing . Wenqiang Wei: Conceptualization ; funding acquisition ; supervision ; validation ; writing – review and editing .This work was funded by the Chinese Academy of Medical Sciences Initiative for Innovative Medicine , the Beijing Nova Program [Z201100006820069].No potential conflicts of interest are disclosed.Ethical approval is not applicable to this study.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Table S1.Table S2Click here for additional data file.


Given the anatomical and functional similarities between the retina and the brain, the retina could be a “window” for viewing brain structures. We investigated the association between retinal nerve fiber layers , and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students  from the i‐Share study. We used multivariate linear models to study the cross‐sectional association of each retinal nerve layer thickness assessed by spectral‐domain optical coherence tomography (SD‐OCT) with structural , and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC‐IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects. In a sample composed exclusively of young healthy subjects, we found significant associations between retinal nerve layers with occipital brain volumes, and with brain diffusion MRI parameters, globally and in regions containing associative, commissural tracts, and in a projection area involved in the visual pathway. Our results suggest common maturational processes in the retina and the brain. Further studies are needed to confirm these results and to study common factors that might explain the parallel maturation of the retina and the brain. The retina is known to be an extension of the brain and could act as a “window” for assess brain structures  as they also included middle aged and older people , which is an ongoing, prospective, and open cohort, started in 2013 and including more than 20,000 participants. The i‐Share cohort aims at evaluating multiple health aspects of university students in France, with an annual follow‐up for at least 10 years  , and 28 subjects with ophthalmological conditions that could affect retinal nerve thickness layers. Finally, the study sample included 857 participants with available brain MRI data and valid OCT data  , included participants did not differ according to age and sex; however, they were more often past or current smokers (26.4% vs. 17.5%).2.2From November 2015 to November 2017, brain MRI scanning was carried out at the Bordeaux University on the same Siemens Prisma 3‐Tesla with a 64‐channels head coil (gradients: 80 mT/m–200 T/m/s). Exclusion criteria were age over 35 years, any contraindications for MRI, or pregnancy. 3D T1‐weighted and 3D FLAIR, together with 100‐direction 2D diffusion‐weighted imaging (DWI) multiband multi‐shell acquisition were performed. This structural acquisition protocol follows in footsteps that of the UK Biobank brain imaging study   , total grey matter (GM) and white matter volumes (WM) and mean cortical thickness (CT), all estimated with Freesurfer 6.0 software. The global GM volume includes cortical volume derived from surface‐based analysis and subcortical structures derived from voxel‐based one. The global WM volume is defined as the volume inside the white surface minus anything that is not WM. Cerebral microstructure was examined through DWI data, used to fit (1) diffusion tensor imaging (DTI) modelling and (2) microstructural model fitting with neurite orientation dispersion and density imaging (NODDI). The diffusion parameters reported in this paper are well‐established markers of brain maturation and ageing: fractional anisotropy (FA) and mean diffusivity (MD); and more recent parameters that have been proposed as potential markers for WM microstructural integrity: peak‐width of skeletonized mean diffusivity (PSMD), neurite density index (NDI) and orientation dispersion index (ODI). FA and MD represent respectively the degree of anisotropy of water diffusion, and the amount of diffusivity in all directions in a given voxel. The PSMD, a novel marker for cerebral small vessel disease   Master 500, Zeiss meditec, Dublin, USA) and spectral‐domain OCT scanning.2 area centered on the optic disc. A 3.46 mm diameter circular scan was automatically performed around the optic disc that provides 256 measurement points of peripapillary RNFL thickness. Macular thicknesses acquisition was also obtained from a 3‐dimensional scan of 6 × 6 mm2 centered on the fovea and the automated segmentation provided by manufacturer software.For each subject, the OCT image acquisition was performed for both eyes without pupil dilatation, using a spectral‐domain OCT scanning  with images acquisition at a speed of 27,000 A‐scans per second and 5 μm axial resolution in tissue. The optic disc cube acquisition is based on a 3‐dimensional scan of 6 × 6 mm2/s.Image quality was assessed by trained technicians and scans with signal strength below 7, motion artefacts and segmentation errors were excluded. In this paper, we analyzed the thickness of the peripapillary retinal nerve fiber layer (ppRNFL), macular ganglion cell‐inner plexiform layer (GC‐ILP) and macular ganglion cell complex (GCC) (which includes GC‐IPL and macular RNFL layers). The right eye was primarily selected for analysis in this study. In case of missing values or invalid retinal measurements in the right eye, the left eye was selected. To note, the scale of the retinal layers, measured in μm, is quite different from the scale of structural MRI markers, such as cortical thickness, expressed in mm, or diffusion parameters , nearby 100 μm2.5Demographic and lifestyle factors including sex, age and smoking status, were collected by a self‐questionnaire at baseline. Blood pressure and anthropometric measures were obtained during medical examination. Systolic and diastolic blood pressures were measured twice using a digital monitor.2.6Firstly, we analyzed associations of retinal markers in relation with global brain MRI markers, namely total GM and WM volumes, mean cortical thickness (averaged from the right and left hemispheres), and mean FA, MD, NDI and ODI metrics from WM skeleton. Secondly, we explored associations of retinal markers with GM and WM markers in different brain regions of the Desikan‐Killiany (for GM volume and CT) or JHU (for DTI and NODDI metrics) atlas, without a priori selection, using averaged measures from the right and left hemispheres. Apart from the PSMD, all the diffusion parameters were available for brain region analysis. Multiple linear regression models were used to explore the association of retinal measurements  with MRI markers . We calculated z‐scores for each retinal nerve layer thickness by subtracting the mean value from the data value and dividing by the standard deviation (SD). The β coefficients correspond to the variation in neuroimaging outcomes for one SD increase in the retinal nerve layer thickness. For brain volumes or cortical thicknesses, positive β coefficient corresponds to a higher volume or thickness. For diffusion‐based metrics, positive β coefficient corresponds to higher anisotropy for FA, higher overall diffusivity for MD, more variability in the diffusivity for PSMD, higher estimated neurite density for NDI, and more complexity in fiber orientation (or less coherence in the fiber organization) for ODI. While biological interpretations of variability in these metrics in healthy young subjects are not straight‐forward, low FA, high MD, high PSMD, and low NDI are typically observed in aging or pathological conditions and often interpreted as signs of compromised WM microstructure. Thus, the association between a thicker retinal nerve sublayer and a better integrity of the WM microstructure is reflected by positive β coefficient for FA and NDI and negative β coefficient for MD, PSMD and ODI.p‐value less than or equal to .05 was considered to be statistically significant.The models were adjusted for sex, age, axial length of the eye , vascular factors , and TIV . Assumptions of regression models were examined graphically, and the linearity of the quantitative independent variables was verified by multivariable fractional polynomial method. When the linearity was violated, dichotomous variables, based on termplots  were used. For regional analysis, Benjamini & Hochberg multiple test correction .33.12 (3.6), and more than a quarter of participants were current smokers (26.4%).The descriptive characteristics of participants are shown in Table The mean retinal nerve layers thicknesses were: 96.5 (9.2) μm for the ppRNFL, 84.3 (6.1) μm for the GC‐IPL and 117.4 (8.4) μm for the GCC. To note, compared with CT , the GCC is about 25 times thinner.3.2p = .04) and GCC (p = .04) and with a trend for ppRNFL (p = .06). Thicker retinal nerve layers were also associated with lower PSMD, significantly for ppRNFL (p = .009) and with a trend for GC‐IPL (p = .05) and GCC (p = .06). For NODDI parameters, only one association was found with thicker ppRNFL associated with lower ODI (p = .02).We found few associations between retinal nerve layers and global MRI parameters Table . No asso3.3p = 10−9) and lingual regions (p = .0286), and a trend was observed for the cuneus region (p = .0645). To note, before the correction, thicker GCC was also associated with greater volume in the pericalcarine region, whereas thicker GC‐IPL was associated with greater volume in the inferior parietal region. No significant associations were found with brain regional cortical thicknesses after multiple testing corrections.Associations between retinal nerve layers thickness and brain regional volumes are displayed in supplementary material Figures S and S3. p = 10−7), while thicker ppRNFL and GC‐IPL were associated with thinner CT in V2–BA18 (p = .05 and p = .01 respectively) , which includes the optic radiations and is linked to the visual cortex. Besides, thicker ppRNFL, GC‐IPL and GCC were associated with higher FA in the sagittal stratum , a major cortico‐subcortical WM bundle. Higher GC‐IPL and GCC thicknesses were associated with higher FA in another association region, the fornix cres or stria terminalis . Figures  and 3. Gp = .002, p = .010 and p = .009, respectively), and lower MD  in the fornix. And thicker ppRNFL was also associated with higher FA in the superior fronto‐occipital fasciculus, and in the brainstem regions  (p = .031 for each of the three).In addition, thicker ppRNFL, GC‐IPL and GCC were associated with higher FA . Although not significant, there were also trends for thicker ppRNFL with higher NDI (p = .064) and thicker GC‐IPL with lower ODI (p = .056) in the same region. Similar pattern was also observed in the fornix, with thicker ppRNFL, GC‐IPL and GCC associated with lower ODI  and thicker GC‐IPL and GCC with higher NDI in the fornix too (p = .036 and p = .017). Figures  and 5. A4This study is, to the best of our knowledge, the first that explored the retina‐brain links in a large sample of healthy young individuals. In this young population, retinal‐brain associations were found mainly with diffusion parameters allowing detecting more subtle changes in the integrity of the WM microstructure. As expected, we found associations with several brain regions involved in the visual pathway. In this pathway, not only diffusion parameters, but also GM volumes were associated with retinal layers, likely reflecting more direct and strong associations. However, our results have shown associations beyond visual regions, mainly in regions containing association fiber bundles, suggesting that retinal layers may reflect brain WM microstructure more globally, and not limited to the fibers of the visual pathway. This is reinforced by the associations we founded between retinal layer thickness and global diffusion parameters. Altogether, these findings support our hypothesis that retinal layer might reflect brain microstructure in young people.Most of the previous studies on the associations between retinal layers and brain MRI markers analyzed global or regional tissue volumes rather than the microstructure of the WM. In non‐diseased populations, they found that thinner retinal layers was associated with atrophy in the brain  and in the Rhineland Study (n = 2872) , thinner ppRNFL and GC‐IPL were associated with reduced subcortical volumes, but also with reduced cortical surface and thinner average cortex , but only with diffusion parameters reflecting WM microstructure. Apparent discrepancies with prior studies may be due to differences in the age of the included populations; indeed, the associations in middle‐aged and older adults could reflect age‐related neurodegenerative processes.Beyond the analysis of brain morphometric measures, very few studies have examined WM microstructural properties in relation to retinal layers. In our study, thicker retinal nerve layers were associated with higher WM microstructural integrity patterns  globally and in several areas including: (i) the post thalamic radiation region, which is involved in the visual pathway; (ii) the sagittal stratum  region composed of projection and association fibers including the inferior longitudinal fasciculus (IFO) and the inferior longitudinal fasciculus (ILF), that are neighbor tracts of the optic radiation  globally , should reflect more the condition of the cerebral GM, while the ppRNFL, which is composed of axons, may reflect more the cerebral WM condition. This assumption was based on the physiological composition of the retinal layers and on one previous study showing that thinning of the GC‐IPL was associated with GM, but not WM volume  across the adult lifespan confirmed these patterns of age‐related DTI changes . It has also received grants from the Nouvelle‐Aquitaine Regional Health Agency , Public Health France , and The National Institute against cancer INCa . The funding bodies were neither involved in the study design, or in the collection, analysis, or interpretation of the data. Digital Public Health Graduate Program, with funding support from the framework of the PIA3 (Investment for the future). Project reference 17‐EURE‐0019.Sara Cristina Lima Rebouças, Fabrice Crivello, Ami Tsuchida, Christophe Tzourio and Catherine Helmer have no conflicts of interest to disclose. Cédric Schweitzer is consultatnt for Abbvie, Alcon, Glaukos, Nicox, Théa, Horus, Johnson & Johnson, Santen and Bausch & Lomb. Jean‐François Korobelnik is consultant for Abbvie, Apellis, Bayer, Janssen, Nanoretina, Roche, Théa and Carl Zeiss Meditec. Cecile Delcourt is consultant for Allergan, Chauvin‐Bausch+Lomb, Laboratoires Théa and Novartis.All participants signed a written informed consent form.Data S1: Supporting Information.Click here for additional data file.


Hematologic malignancy is a risk factor for severe coronavirus disease 2019 (COVID‐19) in adults; however, data specific to children with leukemia are limited. High‐quality infectious adverse event data from the ongoing Children's Oncology Group (COG) standard‐risk B acute lymphoblastic leukemia/lymphoma (ALL/LLy) trial, AALL1731, were analyzed to provide a disease‐specific estimate of SARS‐CoV‐2 infection outcomes in pediatric ALL. Of 253 patients with reported infections, the majority (77.1%) were asymptomatic or mildly symptomatic (CTCAE grade 1/2) and there was a single COVID‐19‐related death. These data suggest SARS‐CoV‐2 infection does not confer substantial morbidity among young patients with B‐lymphoblastic leukemia/lymphoma (B‐ALL/LLy). B‐ALL/LLyB‐lymphoblastic leukemia/lymphomaCOGChildren's Oncology GroupCOVID‐19coronavirus disease 2019CRFcase report formsDSDown syndromeNCINational Cancer Institute1Coronavirus disease 2019 (COVID‐19) causes substantial morbidity, particularly in high‐risk populations . Data onAALL1731 is a phase 3 randomized clinical trial (NCT03914625) for children with National Cancer Institute (NCI) standard‐risk (SR) B‐lymphoblastic leukemia (B‐ALL), and children and young adults with stage 1–2 B‐lymphoblastic lymphoma (B‐LLy) and Down syndrome (DS) B‐ALL that opened in June 2019, and has accrued subjects near the expected rate despite the COVID‐19 pandemic. Children's Oncology Group (COG) protocols account for a substantial and representative proportion of children in the United States with cancer; therefore, data from this trial provide a unique, population‐based assessment of the spectrum of SARS‐CoV‐2 infections in pediatric B‐lymphoblastic leukemia/lymphoma (B‐ALL/LLy) .2AALL1731 includes children 1–10 years old with newly diagnosed NCI SR B‐ALL. Patients with DS younger than 31 years old are eligible for this trial regardless of presenting white blood cell count, as are patients with B‐LLy. In March 2020, the protocol was amended to mandate reporting of all grades of SARS‐CoV‐2 infection, graded according to the CTCAE v5 grading schema for “infections and infestations‐other.” For all infectious adverse events (AEs) on the trial, there is a case report form (CRF) for additional data on the infectious pathogen, and real‐time review of infectious AEs by pediatric oncologists to resolve incomplete or ambiguous submissions .All patients enrolled on AALL1731 from June 28, 2019 to December 31, 2021, who had a reported SARS‐CoV‐2 infection, were included in these analyses. Demographic and treatment data were abstracted from CRFs. Body mass index (BMI) percentiles were based on anthropomorphic measurement at the start of the reporting period in which SARS‐CoV‐2 infection was documented. Statistical comparisons were performed using Wilcoxon rank‐sum and Fisher's exact tests for continuous and categorical variables, respectively.3As of December 2021, 253 patients at 113 centers experienced SARS‐CoV‐2 infections, accounting for 9.4% of all patients receiving protocol therapy during this time. Clinical characteristics are reported in Table The majority of patients  had CTCAE grade 1 or 2 infections, defined as either asymptomatic or mild infections requiring local intervention only. A minority, 58 (22.9%) patients, experienced more severe COVID‐19 (grades 3–5). One patient died with COVID‐19. This individual had multiple prior infectious complications of therapy including sepsis, meningitis, and acute hypoxic respiratory failure and had been removed from protocol therapy after induction. The child was diagnosed with SARS‐CoV‐2 infection via polymerase chain reaction (PCR) while receiving interim maintenance chemotherapy and died 3 weeks later with respiratory failure. Two patients experienced grade 4 infection, defined as “life threatening,” both while in maintenance. The remaining patients were grade 3, requiring or prolonging hospitalization but not immediately life‐threatening. Another grade 3 or higher infectious AE was reported within 7 days of grade 3–5 SARS‐CoV‐2 infection in 28 instances, including febrile neutropenia in 20, lung infection in two, enterocolitis in four, and sepsis in four.p ≤ 0.001). The majority (73.7%) of these delays were prior to the start of maintenance. The impact of this delay on outcomes is unknown as the trial is ongoing.Univariable associations with severe disease are shown in Table p = 0.39).DS did not appear to be a risk factor for infection or for severe disease. Of patients with DS, a similar proportion  had a SARS‐CoV‐2 infection reported. Patients with DS accounted for 10.3% of those with severe infection compared to 6.7% without , nor does it capture COVID‐19 treatment, which may have modified disease severity. Importantly, this cohort largely pre‐dated access to vaccination for children younger than 10, as well as the Omicron variant. As both factors decrease the severity of infection, we anticipate that the tendency toward less severe infection in pediatric B‐ALL/LLy patients will persist as vaccine access expands and virus virulence decreases .Low rates of severe COVID‐19 on AALL1731 support guidance to prioritize treatment of the underlying leukemia, even in the setting of a documented SARS‐CoV‐2 , 17. TheCaitlin W. Elgarten, Joel C. Thompson, Tamara P. Miller, and Sarah Alexander designed the research study. Research was performed by Caitlin W. Elgarten, Joel C. Thompson, Tamara P. Miller, Sarah Alexander, Mignon L. Loh, Elizabeth A. Raetz, Sumit Gupta, Rachel E. Rau, Anne Angiolillo, and Karen R. Rabin. John A. Kairalla and Cindy Wang performed the data analysis. Susan Conway as one of the people who performed the research. Caitlin W. Elgarten and Sarah Alexander interpreted the data and wrote the manuscript. All authors critically reviewed the manuscript.NIH, Grant Numbers: U10 CA98543 (COG Chair's Grant), U10 CA180886 (NCTN Operations Center Grant), U10 CA98413, and U10 CA180899 (COG Statistics and Data Center Grants); St. Baldrick's Foundation; NHLBI Career Development Award, Grant Number: 1K23‐HL161309‐01The authors declare they have no conflicts of interest.These data were collected on AALL1731 (NCT03914625), an ongoing clinical trial approved by the Cancer Therapy and Evaluation Program, the Pediatric Central Institutional Review Board (IRB), and participating center IRBs. Written informed consent and assent (if applicable) are obtained before study entry.Written informed consent and assent (if applicable) are obtained before study entry.


Non‐verbal cognitive ability predicts multiple important life outcomes, for example, school and job performance. It has been associated with parieto–frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non‐verbal cognitive ability in 165 5‐year‐old participants  from the FinnBrain Birth Cohort study. T1‐weighted brain magnetic resonance images were processed using FreeSurfer. Non‐verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool and Primary Scale of Intelligence (WPPSI‐III). In vertex‐wise general linear models, PIQ scores associated positively with volumes in the left caudal middle frontal and right pericalcarine regions, as well as surface area in left the caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non‐verbal cognitive ability in a large sample of typically developing 5‐year‐olds. The findings are generally in line with prior findings from older age groups, with the important addition of the positive association between volume / surface area in the right medial occipital region and non‐verbal cognitive ability. This finding adds to the literature by discovering a new brain region that should be considered in future studies exploring the role of cortical structure for cognitive development in young children. The structural cortical correlates of non‐verbal ability in typically developing 5‐year‐olds were for the most part similar to those seen in older participants in earlier studies. The novel finding in our study was the positive association between volume/surface area in the right medial occipital region and non‐verbal ability. Ultrasound‐verified pregnancy and a sufficient knowledge of Finnish or Swedish languages were required for participation. The cohort study includes several follow‐up studies. Those participants that attended neuropsychological and neuroimaging visits as part of the 5‐year data collection were included in this study.The participants are a part of the FinnBrain Birth Cohort Study  of the participants attended the visit within the intended age range). The exclusion criteria for the neuroimaging study were: (1) born before GW 35 , (2) developmental anomaly or abnormalities in senses or communication , (3) known long‐term medical diagnosis , (4) ongoing medical examinations or clinical follow up in a hospital , (5) child use of continuous, daily medication , (6) history of head trauma , (7) metallic (golden) ear tubes , and routine magnetic resonance imaging (MRI) contraindications.n = 173/203, assessed by Elmo P. Pulli as described in Pulli et al., n = 166/173) were included. Additionally, one participant was excluded due to scoring below 4 scaled score in verbal ability test Similarities and below the standard score 70 assessed by the performance intelligence quotient , leaving us with a final sample size of 165 participants. Of this sample, 115 (70%) were scanned before the start of the COVID‐19 pandemic. After a small break, the visit continued until March 2021. A few participants were missing one of the non‐verbal tasks, and missing data were not imputed. Consequently, the sample sizes were 164 for the Matrix Reasoning task, 160 for the Block Design task, and 159 for PIQ. None of the participants had started in formal education (primary school) as per the Finnish school system. The characteristics of the final sample (n = 165) are displayed in Table For this study, only participants with an adequate quality T1 image (2.22(2) = 30.94, p < .001), a lower monthly income (χ2(3) = 11.65, p = .009) and were younger (t (1286) = −4.130, p < .001) compared to the mothers in the families that participated in the neuropsychological visits.Mothers of the children who did not participate in the neuropsychological visits (out of the 1288 contacted families) had a lower education level (χt (369) = 1.97, p = .047) but did not differ in education level or monthly income compared to the mothers in the families that participated in the MRI visit.Mothers of the children who participated in the neuropsychological visits but not in the neuroimaging visits were older  and executive functioning and self‐regulation, of which only the non‐verbal tasks from the general cognitive ability assessments are used in the current study.The approximately two‐hour‐long study visits were conducted and video recorded by graduate students in quiet examination rooms and the data collection was overseen by PhD students/psychologists. The graduate students were trained by PhD students/psychologists prior to data collection, to ensure unified test administration among all students, and to ensure that the students had sufficient interaction skills to scaffold the children's motivation and mood during the study visit. Written informed consent was provided by the parents prior to the study visit, and the parents received feedback of the child's performance on some of the assessment methods after the study visit.2.5Non‐verbal ability was assessed using the Finnish version of WPPSI‐III, which is a standardized and widely used measure of cognitive ability in young children from ages 2 years and 6 months to 7 years and 3 months , between PIQ and Matrix Reasoning was 0.711 (p < .001), and between Block Design and Matrix Reasoning was 0.164 (p = .039). Scatter plots of the cognitive measurements are shown in Figure The PIQ scores were: mean = 104.7, SD = 15.4, range 68–146. Block Design scores: mean = 10.5, SD = 3.3, range 3–19. Matrix Reasoning scores: mean = 10.8, SD = 2.8, range 1–18. These results suggest a normally distributed cognitive ability in the sample of the present study. The Pearson correlation between PIQ and Block Design was 0.809  sequence. The study visit protocol has been described in more detail in our earlier work , there were 13 participants with incidental findings (6.4%). Of them, 11 were included in the sample of this study (n = 165). Among the 11 incidental findings in this study, there were eight cerebellar anomalies , one vascular anomaly, and two pineal cysts. None of these affected the cortical analyses in this study.All images were viewed by one neuroradiologist (R.P.) who then consulted a pediatric neurologist (T.L.) when necessary. The protocol with incidental findings has been described in our earlier work  Diffusion Tensor Imaging (=DTI) sequence .Participants were scanned using a Siemens Magnetom Skyra fit 3T with a 20‐element head/neck matrix coil. We used Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique to accelerate image acquisition . The scanning protocol (maximum length 60 min) included a high‐resolution T1‐weighted Magnetization Prepared RApid Gradient Echo (=MPRAGE), a T2‐weighted Turbo Spin Echo (=TSE), a 7‐min resting state functional MRI, and a 96‐direction single shell (b = 1000 s/mm2.8http://surfer.nmr.mgh.harvard.edu/). We selected the T1 image with the least motion artefact  and then applied the “recon‐all” processing stream with default parameters. It begins with transformation to Talairach space, intensity inhomogeneity correction, bias field correction  and we decided to use age at MRI scan as the age variable of interest. Maternal age at term covaries with both cognitive ability  and the missing ones were imputed using the mean of other answers from that questionnaire. Finally, paternal education was only available for 108 participants. This data were not imputed, but instead the sensitivity analysis for paternal education level was done using only the participants with available data.2.10Statistical analyses concerning confounders, demographics, and regions of interest (ROI) were conducted using the IBM SPSS Statistics for Windows, version 27.0 . Scatter plots and the related statistics were created using JASP version 0.16.1.0 , the zero‐order correlations were calculated for continuous variables ) and independent samples t‐tests for sex and maternal education level. We report raw n = 165) and those that were excluded (n = 38) were performed with independent samples t‐tests for continuous background factors and chi‐square tests for categorical factors. All background factors from Table To assess potential selection bias, comparisons between the neuroimaging participants that were included in the final sample n = 65 and th2.10.1www.surfer.nmr.mgh.harvard.edu). Qdec is a graphical user interface for a statistics engine running a vertex‐by‐vertex general linear model (GLM). For display purposes, we used the standard FreeSurfer's fsaverage in MNI305 space . We tested for clusters with statistically significant associations between non‐verbal ability and cortical GM volume, surface area, and cortical thickness. The data were smoothed with a kernel of 10 mm full width at half maximum. A Monte Carlo Null‐Z Simulation was run with a z‐value threshold of 1.3, corresponding to p = .05 , a single‐binary application included in the FreeSurfer software suite  between (1) all cognitive measurements , and (2) a multitude of brain metrics, including volume, surface area, and cortical thickness in all 68 ROIs in the Desikan–Killiany atlas  that were included in this study (n = 165) had higher gestational age at birth  and fewer had a NICU stay ; excluded 25 no, 11 yes (30.6%); χ2(1) = 0.016), and their mothers had lower pre‐pregnancy BMI , compared to those who were excluded (n = 38).The children from the neuroimaging sample  and the right pericalcarine region . There were no significant correlations between Block Design scores and brain volumes. For Matrix Reasoning, there was a significant cluster in the right pericalcarine region .Figure 3.32, peak coordinates −36.1, 0.6, 31.0), the left inferior temporal gyrus , and the right lingual gyrus . There were no significant correlations between Block Design scores and surface area. For Matrix Reasoning, there were significant clusters in the left caudal middle frontal gyrus  and the right lingual gyrus .Figure 3.42, peak coordinates −36.8, −18.3, 64.5) and the right postcentral gyrus . There were no significant correlations between Matrix Reasoning scores and cortical thickness.Figure 3.5The correlations between PIQ and brain metrics are shown in Figures 2, peak coordinates −7.1, 37.9, 25.1) that survived correction for multiple comparisons using Monte Carlo Null‐Z simulation . All raw p‐values are presented in Table There were no correlations between non‐verbal ability and either cortical volume, area, or thickness that survived correction for multiple comparisons .The zero‐order Pearson correlations between age at scan and mean cortical thickness were 3.8We further visually explored the regions where PIQ associated with brain metrics to assess whether either subtest was driving the results. Figure Figure 4In this study, we examined the associations between non‐verbal ability and cortical brain structure  in a sample of typically developing 5‐year‐olds. We hypothesized based on the P‐FIT model that non‐verbal ability would be positively correlated with volume and surface area in frontal and parietal regions. In line with the hypothesis, we found that the volume and surface area of the left caudal middle frontal gyrus were positively associated with non‐verbal ability. Additionally, we found significant positive associations with right medial occipital structure and left inferior temporal surface area. Furthermore, we expected to find associations between non‐verbal ability and cortical thickness in frontal and parietal regions. Two significant positive associations with visuospatial ability measures utilizing only one task were found but there were no associations with the overall non‐verbal ability of the child. Altogether, this is the first study to examine the cortical structural correlates of non‐verbal ability in a large sample of typically developing 5‐year‐olds. Our results suggest that some of the structures identified in studies of older participants are correlated with non‐verbal ability at this stage of development.We found associations between non‐verbal ability and both volume and surface area in the caudal middle frontal gyrus. The middle frontal gyrus is a region that is often associated with cognitive ability both structurally  and found both to be associated with cortical thickness in the left frontal cortex, suggesting a common underlying neurobiology between visual abstract reasoning and visuospatial ability  score and cortical thickness in 6–18‐year‐olds, also in older and younger halves separately  cortical thickness was positively associated non‐verbal ability but only when controlling for paternal education level. These regions are part of the P–FIT model (Frangou et al., Vertex‐wise and ROI‐based statistics were explored separately, and the ROI‐based analyses yielded no significant results. The significant clusters were relatively small compared to many Desikan–Killiany ROIs. Furthermore, many of the clusters appeared at the junctions of different ROIs. Therefore, effects would have to be large, for it to be reflected in the average value of the larger ROI. Based on our results, the structural correlates of non‐verbal ability are local and hence tentatively better captured by data‐driven clusters than predefined ROIs. Future studies could extend our findings by exploring functional differences associated with cognitive abilities.Both a strength and a limitation of this study is the limited age range in a cross‐sectional setting. While the strength lies in the possibility to understand the neural correlates of non‐verbal ability at this specific age relevant for later development, it precludes true longitudinal and developmental interpretations. Especially with cortical thickness, it seems to be the case that longitudinal modeling is needed to find the potential individual differences in growth trajectories and how they might relate to non‐verbal ability. This was evident in the moderation analysis, where we did not find a correlation between age and cortical thickness, most likely because the age range was so limited that the trend of decreasing cortical thickness (Bethlehem et al., 5To the best of our knowledge, this is the first study to explore cortical structural development in relation to non‐verbal ability in a large sample of typically developing 5‐year‐olds. We found that non‐verbal ability was associated with volume and surface area in left middle frontal and right medial occipital regions, and especially the medial occipital region was associated with visual abstract reasoning rather than visuospatial ability. On the other hand, cortical thickness in left precentral and right postcentral gyri were only associated with visuospatial ability specifically. Discrepancy between cortical thickness results and other results is not surprising considering that cortical thickness develops relatively independently from surface area and volume (Winkler et al., Elmo P. Pulli: Conceptualization, formal analysis, investigation, visualization, writing—original draft. Saara Nolvi: Conceptualization, supervision, writing—original draft. Eeva Eskola: Writing—original draft. Elisabeth Nordenswan, Eeva Holmberg, Anni Copeland, Venla Kumpulainen, Eero Silver, Ekaterina Saukko: Investigation, writing—review & editing. Harri Merisaari: Methodology, software, writing—review & editing. Jani Saunavaara: Methodology, writing—review & editing. Riitta Parkkola, Tuire Lähdesmäki, Eeva‐Leena Kataja, Riikka Korja: Writing—review & editing. Linnea Karlsson, Hasse Karlsson: Conceptualization, funding acquisition, project administration, writing—review & editing. Jetro J. Tuulari: Conceptualization, funding acquisition, methodology, supervision, writing—original draft.The authors declare no conflicts of interest.Figures S1–S10.Click here for additional data file.Table S1.Click here for additional data file.Table S2.Click here for additional data file.


The pigs were monitored for growth and screened for exposure to four important respiratory pathogens: porcine circovirus type 2 (PCV2), porcine reproductive and respiratory syndrome virus (PRRSv), Mycoplasma hyopneumoniae (M. hyo), Actinobacillus pleuropneumoniae (App) using ELISA tests. Farm management practices were recorded and used to generate management level scores. Treatment expenses incurred were recorded throughout the study. A mixed effects model was fitted to quantify effects of respiratory and helminth infections on average daily weight gains (ADGs), with farm and pig as random effects. Analysis of variance (ANOVA) was used to determine differences in mean treatment costs by farm management standard. Financial losses were estimated from average carcass dressing percentage, ADG reductions during fattening (200  days). Results showed a grower pig in a given farm exposed to PRRSv and Ascaris spp. had significantly lower ADG by 17.10 gr/day and 16.80 grams/day respectively, compared to a similar unexposed pig (p < 0.05). Mean treatment costs per pig declined significantly with increase in management standard scores (MSS), from USD 1.13 per pig in MSS 1 (poor management) farms to USD 0.95 for MSS 3 (better management) farms (p < 0.05). We show that monetary losses due to PRRSv and Ascaris spp. infection amounted to USD 6.6 ± 2.7 and 6.50 ± 3.2 (Mean ± SEM) per pig, respectively during 200 days of fattening. This study strengthens evidence that improving management practices to reduce infections mitigates economic losses. To guide interventions, further studies are required to unravel the full extent of indirect economic losses.This study sought to quantify direct economic losses due to respiratory and gastrointestinal (GI) helminth infections in domestic pigs in Uganda. In a longitudinal study design with repeated measures, farm visits were made at 2 month intervals from October 2018 to September 2019. Weaner and grower pigs ( EconomiEconomic losses due to swine diseases result from mortalities, reduced weight gains , poor reM. hyo, App and Streptococcus suis) have been reported  due to exposure to selected respiratory pathogens and GI parasites. Furthermore, it sought to determine effect of management level on treatment costs.2.2.1.This study was conducted in four selected subcounties of Lira district, mid-northern Uganda, characterized by predominantly smallholder production systems. The selection of subcounties was based on the dominant value chain domain, as described in a previous study . In this2.2.The study was done as a prospective cohort study from October 2018 to September 2019. Live weight measurements and blood sample data was collected at 2 month intervals. We targeted all farms with ≥2 weaner or grower pigs during the study, to reduce losses to follow up. A rolling recruitment procedure was used to enroll pigs for the study, because not all 2 pigs in each farm were available initially. For pigs which died or were sold, new pigs of approximately same weight and age from the same herd were enrolled.2.3.We monitored farms in the urban and Peri-urban settings only because this was where we could find enough accessible confined pigs. We sampled only confined pigs (housed or tethered) because roaming pigs were difficult to capture and monitor. In each farm, we targeted to sample at least 2 weaner (2–3 months) and/or grower pigs (4–6 months). From a sampling frame of a list of pig keeping households obtained from the district veterinary office, random sampling was done. Informed consent was obtained from pig owners to participate in the study and find out their willingness to keep pigs for ≥6 months. In each farm, weaner and grower pigs were randomly sampled. Enrolled pigs were identified by ear tagging and data recorded at the onset and on subsequent visits.2.4.s = 0.06), estimated common variance for the two groups, σ2 = 0.49. To quantify effects of pathogen exposure on ADGs, a sample size for repeated measures design was calculated from The following assumptions were made based on a study by Carter et al. : a normaZα/2 is the standard normal deviate for α = 1.96, βZ = −0.84, and μ1 − μ2, the detectable difference in mean ADGs between unexposed and exposed pigs, taken to be 67 gr/day (d = 0.067) and the number of time points, n = 3. The assumed correlations of repeated measures, ρ = 0.6. Using this equation, the total required sample size (n) for the study, n = 197 pigs.where 2.5.2.5.1.®HiWeigh BSR5300 weighing scale , feed quality grade used based on crude protein (CP) content: grade 1 = sole grazing only (2–7%), grade 2 = sole grazing and maize bran (7–10%), grade 3 = maize bran and swill (11–14%), and grade 4 = compounded (15–18%) feed , pig age, sex, breed , live weight (kg) measured using a 8%) feed , 28. BodTo estimate direct production costs, treatment expenses throughout the study were recorded. Drug treatments were recorded per herd and divided by herd size to estimate average cost per pig during the 60 day sampling interval. These included types and cost of drugs bought  and veterinary fees. Herd size and perceived herd value (based on farm-gate prices) were recorded at the start and at each visit. Expenses and value of pigs were recorded in Uganda shillings. Fixed and other variable costs  were omitted because of lack of reliable records.2.5.2.® Livestock, Shandong, ChinaModel BZ002, MG), while smaller pigs were restrained by hand. With a pig properly restrained, blood was collected from the cranial vena cava or jugular vein, using a 21G, 1.5″ needle into 5 mL plain BD® vacutainer tubes. The tubes were labeled with pig details and placed in icebox at 4–6°C. Shortly after collection, samples were delivered to Lira district veterinary laboratory for temporary storage. Blood samples were left to stand at room temperature (20°C) overnight and serum harvested the next day into 2 mL cryotubes , labelled and stored in a fridge at −20°C until testing.Pigs were monitored for exposure to 4 respiratory pathogens by blood sampling at each visit. Larger pigs were restrained with a metallic pig catcher , Makerere University. Eggs were hatched (Baermann’s method) and larvae used to identify helminth species  were collected from the rectum of each pig using gloved hands into 5 mL plastic containers, labelled and placed in ice box at 4°C. Samples were screened for presence or absence of  species .2.5.5.Data was cleaned, coded, and analyzed using R Statistical software . To quanlme4,” “lmer,” “lmerTest,” “reghelper”, and “jtools” . The R packages ““jtools”  were useβ0, β1 are the fixed effect coefficients for the intercept and slope respectively; Xi,kiX are the fixed effects regressors , μfarm(j) is a random effect of farm i, and the residual error term,N  was derived from the method of . The model was fitted using Residual Maximum Likelihood (REML) estimation procedure and selection was done using Akaike Information Criterion (AIC). Model diagnostics were evaluated by plotting a graph of fitted values versus residuals.As farm management practices directly or indirectly affect pathogen exposure, variables known or suspected to influence exposure rates and weight gains were captured. A pig ethod of  used as 3.n = 53), while females accounted for 43.6% (n = 41).r = 0.37, t = 11.8, p < 0.000), live weights and visits , and between live weights and pig age . The mean live weight (Mean ± SD) of pigs in this study was found to be 18 ± 6.9 kg for pigs of 2–4 months, 29.7 ± 11.8 kg for pigs of 5–8 months and 47.4 ± 15.5 kg for pigs of 9–15 months. This suggests pigs were generally underweight for their age, as reflected by poor body condition scores (from data). The value of pigs sold, and mortality observed varied between subcounties  out of 864 pigs raised from 94 farms (11.5%) were reported to have died during the study, whose perceived total value at farm-gate price before death was UGX 13,200,000 . However, total mortalities reported were due to several causes, which were indistinguishable from each other.3.1.n = 46) housed pigs, while the rest, 51.1% (n = 48) raised pigs by tethering near homesteads , floor type, isolation of sick pigs, access to extension services and routine use of dewormers and antibiotics , 36. Usimesteads .F value = 4.384, p = 0.012). High and moderate farm management standard farms showed significantly lower mean treatment costs compared to MSS 1 (poor) farms  farms . Table 53.2.χ2 = 17.20, p < 0.0001***). The mixed effects model showed infection with PPRSv and Ascaris spp. were marginally significant. Between-farm variance (Mean ± SD: 2636.2 ± 51.34) explained the greatest variation in ADGs (84.5%), followed by between-pig variance, 15.5% (Mean ± SD: 480.9 ± 21.93).We fitted a linear mixed effects model to predict ADG with farm and pig as random effect terms (1 | Farm_ID) + (1 | Farm_ID: Pig_ID). A qqplot and a plot of residuals (distributed around mean zero) vs fitted values were used to verify the normality of data. Starting with a null model, predictors of ADG were added. Confounding was tested by inclusion and exclusion of variables and observing changes in model coefficients. Interactions between variables  were tested but found non-significant and thus dropped from the final model. Based on AIC and BIC, the model  provided4.This study highlights the role of farm management and the impacts of respiratory infections on pig daily weight gains in Uganda. To support farm decision making, a clear understanding of farm management practices and their relationship with weight gains and production costs is required. Ultimately, the goal of any producer is to minimize production costs, reduce or eliminate economic losses, which translate into better profit margins. These findings compare favorably with other studies elsewhere, which reveal negative effects of respiratory diseases on daily weight gains , 39, incAscaris spp. gained less  ADGs compared to those unexposed  could be explained by endemic infections, underfeeding and inferior genetics as reported in recent studies in Uganda , 43, 45.MSS 3) spent significantly lower mean treatment costs compared to those with poor (MSS 1) management level  were not captured. In this study, partly due to underfeeding, farmers often kept pigs for longer than 200 days (6.5 months), which adds to possible losses from extra feeds needed to raise pigs to market weight. In the US,  influences pig welfare and hygiene. Farmer attitude and behaviour determines the frequency with which wastes are removed from pens. These management factors directly influence the pathogen load that may accumulate and multiply in pens, particularly if the floor was poorly designed. Accumulation of pathogens in pens due to lack of cleaning may facilitate transfer of infection(s) between pigs. Cargill  reportedAscaris suum infection than regular deworming  attributable to deaths and salvage prices of sick pigs due to lack of reliable farm data. That these indirect costs were not captured suggests economic losses encountered by farmers in this study may have been underestimated. Besides, because not all farmers treated their pigs despite showing clinical disease due to lack of cash or no access to extension worker, it’s probable that errors in estimates of treatment costs may have been introduced. Because ELISA tests used reflect prior exposure of pigs to respiratory pathogens, knowledge gaps remain on the duration of infection(s), and the time lapse to induce clinical disease during individual or mixed infections, both of which ultimately influence growth rates.5.Ascaris spp. infection had significantly lower daily weight gains (ADG) by 17.10 gr/day and 16.80 grams/day respectively, compared to a similar unexposed pig of the same age. Mean treatment costs per pig declined with improvement in management level scores (MLS), from USD 1.13 in MLS 1 (poor management) farms to USD 0.95 per pig for MLS 3 (better management) farms. We show that monetary losses encountered by farmers due to PRRSv and Ascaris spp. infection amounted to USD 6.60 ± 2.7 and 6.50 ± 3.2 (Mean ± SEM) per pig, respectively during 200 days of fattening.To the best of our knowledge, this is the first study in Uganda to document evidence of adverse effects of respiratory and helminth infections on weight gains in pigs. We showed that a grower pig in a given farm exposed to PRRSv and This study highlights the role good management plays in mitigating against adverse effects of respiratory infections in pigs. To reduce possible economic losses from disease, it is important for farmers to adopt good herd management practices using welfare concept, which include proper housing, nutrition, and biosecurity. These prerequisites are necessary for optimal growth and health of pigs, which enhances farm profitability. Further studies are required to establish the full extent of other possible indirect losses  considering pathogen interactions and variations in disease severity.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The animal study was reviewed and approved by ILRI’s Institutional Research Ethics Committee (IREC no. IREC2018-23) ILRI’s Institutional Animal Care and Use Committee (IACUC2018-22) Institutional Review Board (IRB no. SBLS/REC/18/008), College of Veterinary Medicine, Animal Resources and Biosecurity (CoVAB), Makerere University. Written informed consent was obtained from the owners for the participation of their animals in this study.MD, PO, JE, FM, BW, and EO designed the study. PO performed data collection. PO, MD, and EO analyzed data. PO and EO prepared draft manuscript. All authors contributed to the article and approved the submitted version.Sustainable Animal Productivity for Livelihoods, Nutrition and Gender (SAPLING) inclusion”  and all donors and organizations which globally support its work through their contributions to the CGIAR Trust Fund https://www.cgiar.org/funders/trust-fund/. The support received from the German Academic Exchange (DAAD) Service  for Peter Oba’s PhD programme at Makerere University is acknowledged.We acknowledge funding support from the CGIAR Research Programme on Livestock and the OneCGIAR initiative “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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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