This study is the first study in which demographic, laboratory data, and outcomes of coronavirus disease‐2019 (COVID‐19) patients due to the circulating SARS‐CoV‐2 infections caused by different variants  are compared in Iran.We conducted a retrospective study of confirmed hospitalized COVID‐19 cases from April 9, 2021, to May 22, 2022. Demographic data and laboratory findings were extracted from patients' electronic medical records on the first day of admission to the hospital. All patients were followed up for outcomes related to COVID‐19 including intensive care unit (ICU) admission and mortality rate.p < 0.001). The rate of ICU admission during waves 4–6 significantly increased.Of 760 confirmed hospitalized COVID‐19 cases, 362, 298, and 100 represented patients during waves 4–6, respectively. During the Omicron wave, hospitalized patients were older than the other two waves and had a lower median level of C‐reactive protein (CRP), alanine transaminase (ALT), aspartate transaminase (AST), and erythrocyte sedimentation rate (ESR). The median length of hospital stay during waves 4–6 was 5 days (interquartile range [IQR]: 4.0–8.0), 7 days (IQR: 6.0–11), and 6 days (IQR: 5.0–9.0), respectively (Although the Omicron variant caused less severe disease, in older patients who were hospitalized due to Omicron infection, longer hospital and ICU stays were reported, which could be attributed to their old age. In particular, elderly patients are more vulnerable to severe COVID‐19; otherwise, as expected, other laboratory parameters and clinical outcomes were in accordance with differences in pathogenicity and infectivity of these variants. ACE2angiotensin‐converting enzyme 2ALTalanine transaminaseASTaspartate transaminaseCOVID‐19coronavirus disease‐2019CRPC‐reactive proteinESRerythrocyte sedimentation rateIQRinterquartile rangeRBCred blood cellRBDreceptor binding domainSARS‐CoV‐2severe acute respiratory syndrome coronavirus 2VOCsvariants of concernWBCwhite blood cell count1The world has faced a major public health challenge due to the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) as the cause of coronavirus disease‐2019 (COVID‐19) and still, it is unclear when the pandemic will come to a complete end.This aim of this study is to describe the demographic data, laboratory parameters, and different in‐hospital outcomes across the last three major waves of COVID‐19 driven by Alpha, Delta, and Omicron variants in hospitalized patients in Tehran, Iran.22.1We performed a single‐center retrospective study of proven hospitalized COVID‐19 cases in Valiasr Hospital, a COVID‐19 referral center affiliated with Shahid Beheshti University of Medical Sciences (SBUMS) in Tehran, Iran, from April 9, 2021, to May 22, 2022.2) ≥ 94% on room air at sea level. Patients with oxygen saturation (SpO2 < 94%) on room air, oxygen partial pressure (PaO2)/fractional inspired oxygen (FiO2) ratio < 300 mmHg, a respiratory rate > 30 breaths/min, or lung infiltrates > 50% are defined as severe COVID‐19 infection. We also included other criteria such as the need for intubation and admission to the ICU, which were monitored throughout the patients' hospitalization. Patients who were not admitted for COVID‐19 and children younger than 20 were excluded from the study. All patients in three waves received glucocorticoid and anticoagulant therapy.The inclusion criteria for this study were (I) patients over 20 years of age diagnosed with COVID‐19 by real‐time polymerase chain reaction (PCR) and (II) those admitted to the hospital according to moderate to severe COVID‐19 infection. We defined moderate and severe patients according to standard guidelines. Moderate patients are considered to have lower respiratory tract involvement with oxygen saturation  test. Variables with skewed distributions were presented as median (interquartile range [IQR]) and compared using the Kruskal–Wallis test. All statistical analyses were conducted using SPSS Statistics for Windows Version 21. We considered 33.1p < 0.001). Men accounted for the majority of COVID‐19 cases (411 out of 760 [54.2%]) during the study period. However, patients did not differ based on sex among the three waves (p = 0.928).Seven hundred sixty patients hospitalized with COVID‐19 infection were enrolled in the study. Of 760 included patients, 362, 298, and 100 represented patients during waves 4–6, respectively. A total of 102 (13.4%) patients were deceased during this study. Moreover, there were 113 (14.9%) admissions to the ICU and 647 (85.1%) to the wards. The majority of those who died (49 out of 102 [48%]) were over the age of 70. During the fourth, fifth, and sixth waves , the mean ages of hospitalized patients were 58 ± 15.7, 55.5 ± 15.6, and 67.4 ± 17.5, respectively (3.2p < 0.001), blood urea (p = 0.001), creatinine (p = 0.01), aspartate transaminase (AST) (p = 0.008), alanine transaminase (ALT) (p < 0.001), white blood cell count (WBC) (p = 0.005), red blood cell (RBC) (p = 0.001), platelet, neutrophil‐to‐lymphocyte ratio (p < 0.001), and magnesium (p < 0.001) levels of patients between the waves. However, blood sugar (p = 0.3) and calcium (p = 0.06) were not significantly different in patients admitted in the three waves. Statistical analysis showed that patients in wave 6 had significantly higher urea, creatinine, neutrophil‐to‐lymphocyte ratio, and platelet and lower CRP, AST, and ALT than the two other waves.Clinical presentation among hospitalized patients with COVID‐19 did not show significant differences between these three waves. However, the most frequently reported clinical symptoms were fever, dyspnea, cough, chest pain, myalgia, and headache (not shown). The laboratory findings at admission are outlined in Table 3.3p < 0.001). The hospital stay was longer in wave 5 than in the other two waves. There was no significant difference in mortality rate from waves 4 to 6, with 45 (12.4%), 43 (14.4%), and 14 (14%) deaths, respectively (p = 0.7). However, the number of deceased patients during wave 5 was slightly more than in the other two waves. The rate of ICU admission during waves 4–6 significantly increased. Subsequently, the ICU admission rate during waves 4–6 was 13%, 14.4%, and 23%, respectively (p = 0.04). Comparing ICU length of stay showed that the ICU length of stay during wave 6 was longer than the other ones (p = 0.008). The percentage of ICU, deceased patients, and length of hospital stay for the three waves are presented in Figure The median length of hospital stay for the fourth, fifth, and sixth waves was 5 days (IQR: 4.0–8.0), 7 days IQR: 6.0–11), and 6 days (IQR: 5.0–9.0), respectively . Besides, it is the first study in Iran comparing these variables during the fourth, fifth, and sixth waves of COVID‐19. The most noticeable finding of this study was the difference in age, length of hospital stay, ICU admission, and ICU length of stay in the three different waves of COVID‐19 infection. In contrast, there was no difference in in‐hospital mortality among the three different waves. The mean age of hospitalized patients in our study was 58.2 ± 16.3 years. Our study had a higher mean age during the Omicron wave (67.4 ± 17.5) compared with earlier waves, which is in contrast with Yoon et al. and Bouzid et al. who reported that patients infected with Omicron had a lower mean age compared with the Delta variant (62 vs. 67 years and 54 vs. 62 years), respectively.The crucial features of the Delta variant are mutations in the spike protein that confer increased affinity for the angiotensin‐converting enzyme 2 (ACE2) receptors. Among these mutations, S:P681R substitution in the furin cleavage site leads to increase virus spread and infectivity.The Omicron variant has triple mutations at the furin cleavage site  that help it to replicate more and to increase its transmissibility efficiently. The receptor binding domain (RBD) of the viral spike protein has two mutations (T478K and N501Y) associated with increased viral binding affinity. Most mutations are associated with increased pathogenicity and potential evasion of infection‐blocking antibodies.The early finding suggested that the Omicron variant had a reduced ability to replicate in the lung parenchyma, which may have contributed to a reduced disease severity compared with previous variants.Omicron has milder symptoms than the Delta variant, but older people are still at risk of serious complications because their immune system is not as strong and antibody levels are usually insufficient to fight such mutant viruses.2+ that were related to reducing the severity of COVID‐19 symptoms.During the Delta predominance era, a small percentage of Iranians had been fully vaccinated that might be linked to severe symptoms and in‐hospital mortality. In this regard, consistent with previous studies, elevated CRP, erythrocyte sedimentation rate (ESR), ALT, and AST as laboratory parameters for the severity of COVID‐19 were observed in the Delta variant.In the sixth wave by the Omicron variant, regardless of vaccination status, the majority of infected individuals were young asymptomatic outpatients while patients with comorbidities and elderly patients had a high risk for severe COVID‐19 and hospitalization.The previous studies conducted in Iran have investigated the earlier waves of COVID‐19. A retrospective study by Hadadi et al., which was conducted from February 16 to October 28, 2020, during the first, second, and third waves of COVID‐19, found that the length of hospital stay and acute cardiac diseases increased in these waves. Moreover, Jalali et al. revealed differences in epidemiologic features between the second wave and the first wave of COVID‐19 in Babol, North of Iran.It should be mentioned that this study had some limitations that must be emphasized. First, this study was an observational study and has inherent potential bias. Second, some patients' demographic data such as comorbidities were not available so we could not compare these factors between waves. Third, it was a single‐center study on the Iranian population, and future multicenter studies with larger sample sizes are needed.5Despite these limitations, herein, observed differences in patients' characteristics (precisely around 10 years of age difference), laboratory tests, and in‐hospital outcomes of admitted patients during three waves of COVID‐19 driven by different variants were in accordance with the characteristics of the variants that caused these COVID‐19 waves. It should be highlighted that the present study was performed on hospitalized COVID‐19 patients. Consequently, even though Omicron variant caused less severe disease, in older patients who were hospitalized due to Omicron infection, longer hospital and ICU stays were reported, which could be attributed to their old age. Also, variation/rise/decline in some of the lab test results and clinical outcomes could be attributed to aging in hospitalized patients with Omicron infection. In particular, elderly patients are more vulnerable to severe COVID‐19; otherwise, as expected, other laboratory parameters and clinical outcomes were in accordance with differences in pathogenicity and infectivity of these variants.Zahra heydarifard: Investigation; methodology; writing—original draft. Nazanin‐Zahra Shafiei‐Jandaghi: Investigation; methodology; review and editing. Moslem Safaei: Investigation. Forough Tavakoli: Data curation; methodology. Somayeh Shatizadeh Malekshahi: Conceptualization; investigation; methodology; writing—review and editing.The authors declare that there are no conflicts of interest.https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/irv.13184.The peer review history for this article is available at The study was conducted in accordance with the Declaration of Helsinki and national and institutional standards and it was approved by the Valiasr Naja Hospital Medical Ethics Committee. Patients signed written informed consents and accepted the publication of clinical data for research. All methods were performed in accordance with the relevant guidelines and regulations.


The self-face advantage (SFA) is reflected through a faster recognition of a self-face compared with familiar and unfamiliar faces. Nevertheless, as Westerners and East Asians tend to present differences in self-concept styles, it is possible that the SFA is modulated by culture. The present study explored this possibility using a visual search task. British Caucasians and Malaysian Chinese participants were asked to search for frontal view images of self, friend, and unfamiliar faces among an array of unfamiliar faces. Regardless of race, participants were more accurate and faster in searching for the own face and friend’s face compared with an unfamiliar face, with no differences in the search between the own and friend’s face, and these findings could not be accounted by the cultural differences in self-concept . Altogether our results suggest that culture does not modulate the SFA and that this effect is better explained by a familiar face advantage. One’s own face is presumably one of the most distinctive physical features  of an inThe own face receives attentional priority and is processed faster compared with other faces  to show a robust SFA and people with interdependent selves  to show a weakened SFA. To test this hypothesis, this study included British Caucasians and Malaysian Chinese and compared their search times and accuracy for frontal view images of self, friend, and unfamiliar faces among an array of unfamiliar distractor faces. Specifically, we anticipated that Caucasians would demonstrate faster reaction times (RT) and higher accuracy when searching for their own face compared with their friend’s face as independent selves would show stronger attention to self-faces. Conversely, as the interdependent selves place more emphasis on the interconnectedness between self and others, we anticipated that when searching for their own face and friend’s face, Chinese participants would show a smaller difference in terms of the RT and accuracy than Caucasian participants.As both the self-face and the friend’s face are—due to extensive exposure—highly overlearned faces, both faces should show a processing advantage compared with the unfamiliar face in both race groups. For example, In addition, we also explored whether an SFA can be explained by the differences in self-construal of the participants, regardless of their race. The Independent and Interdependent Self-Construal Scale SCS;  was usedAlthough this questionnaire has been widely used in other face processing studies concerning cultural modulation effects e.g., , it has Horizontal patterns of social relationship assume oneself is similar to other selves , whereas vertical patterns comprise of hierarchy and involvement of authority, wherein each self is distinct from other selves  were individually tailored to each participant. Each participant was photographed under similar conditions , in a frontal position while assuming a neutral and a happy expression and while articulating three different speech sounds . All face images were also converted to greyscale. These transformations would minimise differences in non-facial cues.“Self-face” images were presented in a mirror-reversed orientation , whereas the “friend” and “unfamiliar” images were presented in normal orientation. Each participant’s stimuli set consisted of four sets of images: one target self-face set (with five different images), one target friend face set (with five different images), one target unfamiliar face set (with five different images), and six distractor faces sets (each with five different images). not at all likely and 5 = extremely likely. Faces were presented individually in the centre of the screen until participants respond.After the main experiment, a subsequent study was conducted to assess the similarity of the face stimuli across target conditions and race groups. This assessment could only be conducted afterwards because the self-face and friend face stimuli were not available before the visual search task had been completed. Ten independent raters from each race group (who did not participate in the main experiment) were asked to rate how much each of the own-race faces used in the different conditions of the visual search task  stands out. Specifically, they were asked to rate “how likely will this face stand out in a crowd?” on a 5-point Likert-type scale, with 1 = t-tests were conducted with the two stand-out scores as the dependent variables and race group as the independent variable. If any of these t-tests showed differences across race groups, these stand-out scores would be included as covariates in the analyses of the visual search task.With these scores, a “stand-out” score for each face condition compared with the mean stand-out score for the distractor faces was calculated. Specifically, we calculated for each participant how much the familiar faces  stood out compared with distractor faces (SF or FF–DF), and how much the unfamiliar face stood out compared with the distractor faces (UF–DF). Because participants were recruited in pairs, there would only be one set of familiar faces, as the face of each participant would have two roles (self-face and friend face). Finally, two independent-samples t(18) = 1.09, p = .291. Similarly, the stand-out scores between unfamiliar and distractor faces also did not differ significantly across Chinese and Caucasian participants, t(18) = −0.84, p = .414.The stand-out scores between familiar faces (SF or FF) and distractor faces did not differ significantly across Chinese and Caucasian participants, strongly disagree to 7 = strongly agree. Using Cronbach’s alpha, previous research reported that the internal consistency of the interdependent self-construal subscale was .59, whereas the internal consistency of the independent self-construal subscale was .60  that measure the two distinct dimensions of self-construal . Partici was .60 .never or definitely no to 9 = always or definitely yes. Each dimension’s items are summed up separately to create a HI, HC, VI, and VC score.This scale consists of 16 items that measure four different dimensions of collectivism and individualism, namely horizontal (H) and vertical (V) individualism (I) and collectivism (C), making up HI, VI, HC, and VC. Each dimension consists of four items. For instance, an item from HI dimension is “I rely on myself most of the time; I rarely rely on others”; an example item from VI is “It is important that I do my job better than others”; an item from HC is “I feel good when I cooperate with others”; and an item from VC is “It is important to me that I respect the decisions made by my groups.” Participants were required to indicate their agreement with the statements on a 9-point Likert-type scale ranging from 1 = This study used a mixed design with one between-subjects variable (race: Chinese or Caucasian) and two within-subjects variables . A total of six blocks with each target identity condition was presented twice. The presentation of blocks was counterbalanced for target identity  where target identity changed from one block to the next.Each block consisted of a total of 80 trials wherein target faces appeared in only 50% of the trials : 40 (5 different target images × 8 repetitions). The remaining 50% of the trials consisted of display of only unfamiliar distractor faces . The order of trials within each block was randomised as well. The distractor faces were randomly selected among the set of six distractors with no two identical faces presented within the same trial. For each trial, participants’ set of stimuli  would always consist of the same emotional expression, race, and gender. At the start of the study, participants performed a familiarisation phase: 36 practice trials with the same unfamiliar target during the practice trials as during the subsequent test trials.During the experiment, participants were seated 70 cm from the screen. The screen measured horizontally 51 cm and vertically 28.5 cm. Participants were then instructed to search for a given target identity among an array of distractor faces. At the start of each block, participants were cued with a target image . With a key press by the participants, each trial was initiated with a central fixation cross appearing for 500 ms. Participants were asked to fixate the cross until an array of six faces is presented. All face stimuli  were randomly positioned to one of the six possible locations to form a hexagon around a fixation cross subtending to a visual angle of 10.1° × 7.7° see . The disParticipants were also asked to complete the SCS and HVIC questionnaires. They were asked to answer these questionnaires prior to performing the visual search task. The study took about 40 min to complete.As processing efficiency  is our main interest, data analysis was performed on the median RT of correct responses. The median of RT was used instead of mean RT to remove the influence of extreme values. Accuracy was recorded and used an outcome variable as well.Race: Chinese or Caucasian) × 3 (Target Identity: Self (SF), Friend (FF), or Unfamiliar (UF)) × 2 (Target Presence: Present or Absent) mixed-design ANOVAs were conducted on the median RTs and search accuracy for correct responses, with race as the between-subject variable and target identity and target presence as the within-subject variables.To test for the effects of cultural modulation of the SFA, in the first part of the analysis, participants were grouped according to their ethnicity. Two 2  = 118.27, p < .001, ηp2 = .518, with shorter search times for the British Caucasians  than for the Malaysian Chinese  participants. In addition, a significant main effect of target identity was reported, F = 86.23, p < .001, ηp2 = .439 (Huynh–Feldt corrected). Holm–Bonferroni post hoc comparisons indicated that participants searched SF  faster than UF , and searched FF  faster than UF , but there was no significant difference in the search time for the SF and FF . The analysis also revealed a significant main effect of target presence, F = 706.48, p < .001, ηp2 = .865, with participants responding faster in the present trials  compared with the absent trials .F = 12.43, p < .001, ηp2 = .102 (Huynh–Feldt corrected), and between race and target presence, F = 8.12, p = .005, ηp2 = .069. Both two-way interactions were qualified by a significant three-way interaction between race, identity, and target presence, F = 9.68, p < .001, ηp2 = .081 (Huynh–Feldt corrected).The analysis further showed a significant interaction effect between race and identity, F = 11.47, p < .001, ηp2 = .173 (Huynh–Feldt corrected), a significant main effect of target presence, F = 439.23, p < .001, ηp2 = .889, and a significant interaction effect between identity and target presence, F = 7.96, p = .002, ηp2 = .126 (Huynh–Feldt corrected). Holm–Bonferroni post hoc comparisons revealed that in target present trials, participants search the SF  and FF  faster than UF, but there were no significant differences in the search time for SF and FF . In the target absent trials, participants searched the SF faster than UF  but there were no significant differences in the search time between SF and FF  and FF and UF .To understand these interactions further, we conducted simple main effect analyses for each level of race. An ANOVA on the median RT for Malaysian Chinese showed a significant main effect of identity, F = 149.67, p < .001, ηp2 = .731 (Huynh–Feldt corrected), a significant main effect for target presence, F = 277.65, p < .001, ηp2 = .835, and a significant interaction between identity and target presence, F = 3.78, p = .026, ηp2 = .064. Holm–Bonferroni post hoc comparisons revealed in target present trials, SF  and FF  was searched faster than UF, but there were no significant differences in the search time for SF and FF ; in the target absent trials, SF  and FF  was also searched faster than UF, whereas there were no significant differences in the search time between SF and FF .Next, an ANOVA on the median RT for British Caucasians also revealed a significant main effect of identity, In summary, we hypothesised that British Caucasians would search SF faster than FF, demonstrating a robust SFA, but Malaysian Chinese would show a smaller SFA than British Caucasians. However, our results did not support this hypothesis. We also expected that participants would search familiar faces (SF and FF) faster than UF, regardless of participants’ race. Our results supported this hypothesis, as both groups of participants were faster in searching for SF and FF than UF, but no differences were found between SF and SF. Finally, our findings also revealed that compared with Malaysian Chinese, British Caucasians were overall faster in searching for faces, regardless of their identity.F = 18.26, p < .001, ηp2 = .142, with a higher accuracy for British Caucasians  than Malaysian Chinese  participants. A significant main effect of target identity was also found, F = 108.64, p < .001, ηp2 = .497 (Huynh–Feldt corrected). Holm–Bonferroni post hoc comparisons indicated that participants performed significantly better when searching for the SF  and FF  than for the UF , but there was no significant difference between the SF and FF .F = 8.13, p = .002, ηp2 = .069, between race and target presence, F = 8.52, p = .004, ηp2 = .072, and between identity and target presence, F = 15.05, p < .001, ηp2 = .120 (Huynh–Feldt corrected). All two-way interactions were qualified by a significant three-way interaction between race, identity, and target presence, F = 8.88, p = .001, ηp2 = .075 (Huynh–Feldt corrected).The analysis further showed a significant interaction effect between race and target identity, F = 59.36, p < .001, ηp2 = .519 (Huynh–Feldt corrected). The analysis also revealed that identity interacted significantly with target presence, F = 15.28, p < .001, ηp2 = .217 (Huynh–Feldt corrected). Holm–Bonferroni post hoc comparisons revealed that in target present trials, participants searched the SF  and FF  better than UF, whereas there were no significant differences in the search accuracy for SF and FF ; in the target absent trials, participants searched the SF  and FF  better than UF, but there were no significant differences in the search accuracy for SF and FF . The analysis revealed no main effect of target presence, F = 2.90, p = .094, ηp2 = .050.To understand these interactions further, we conducted simple main effects analysis for each level of race. An ANOVA on the accuracy data for Malaysian Chinese showed a significant main effect of identity, F = 55.79, p < .001, ηp2 = .503 (Huynh–Feldt corrected). Holm–Bonferroni post hoc comparisons showed that participants performed better when searching for the SF  and FF  compared with the UF, but there were no significant differences between the search accuracy for the SF and FF . The analysis further revealed a significant main effect for target presence, F = 6.01, p = .017, ηp2 = .099, with a higher search accuracy for absent trials compared with present trials. The analysis also revealed no significant interaction between target identity and target presence, F = 0.89, p = .408, ηp2 = .016.Next, an ANOVA on the accuracy data for British Caucasians revealed a significant main effect of identity, To summarise, we hypothesised that British Caucasians would search SF better than FF demonstrating a robust SFA, and that this SFA would be smaller in Malaysian Chinese participants. Similar to our RT analysis, accuracy results did not support this hypothesis. We also hypothesised that participants, regardless of their race group, would search familiar faces (SF and FF) more accurately than UF. Our results supported this hypothesis. Finally, our findings showed that compared with Malaysian Chinese, British Caucasians were overall more accurate in searching for faces, regardless of the identity.To examine whether the SFA reported can be significantly predicted by the self-construal, regardless of the race of participants, six two-step hierarchical regressions were conducted with the difference in search accuracy or median RT between two target conditions: SF–FF, SF–UF, or FF–UF as the criterion variable. Race of participants was entered in the first step of the regression, whereas self-construal  was entered in the second step. F = 25.01, p < .001, and accounted for 18.5% of the variation of the differences whereas self-construal did not significantly predict the differences, F = 0.11, p = .742. Another hierarchical regression analysis revealed that race contributed significantly to the differences in the search time between FF and UF, F = 26.81, p < .001, and accounted for 19.6% of the variation in the search accuracy of the differences whereas self-construal did not significantly predict the differences, F = 0.27, p = .603. Finally, neither race, F = 0.03, p = .868, nor self-construal, F = 1.41, p = .237, contributed significantly to the differences between the search time of SF and FF.The hierarchical regression analysis revealed that race contributed significantly to the differences in the search time between SF and UF, F = 9.47, p = .003, and accounted for 7.9% of the variation of the differences whereas self-construal did not significantly predict the differences, F = 0.70, p = .403. Another hierarchical regression analysis revealed that race contributed significantly to the differences in the search accuracy between FF and UF, F = 8.14, p = .005, and accounted for 6.9% of the variation in the search accuracy of the differences whereas self-construal did not significantly predict the differences, F = 1.58, p = .212. Finally, neither race, F = 0.89, p = .346, nor self-construal, F = 0.53, p = .467, contributed significantly to the differences between the search accuracy of SF and FF.The hierarchical regression analysis revealed that race contributed significantly to the differences in the search accuracy between SF and UF, We hypothesised that, regardless of the race of the participants, individuals with higher scores on the independent self-construal subscale would show a stronger SFA whereas individuals with a higher score on the interdependent self-construal subscale would show a weaker SFA. However, contradicting the hypotheses, these results suggest that, for both the search time and search accuracy, the reported SFA relative to UF can be explained by participants’ race but not by participants’ self-construal as measured by the level of interdependence and independence on the SCS questionnaire.Finally, due to the low internal consistency of the SCS questionnaire, an additional HCIV questionnaire was administered to provide support to the scores from SCS questionnaire. For this analysis, six two-step hierarchical regressions were conducted to examine whether the SFA effect reported can be significantly predicted by the level of individualism and collectivism of participants. The difference in search accuracy and median RT between of two target conditions: SF–FF, SF–UF, or FF–UF were entered as the criterion variable. Race of participants was entered in the first step of the regression, whereas HI scores, VI scores, HC scores, and VC scores were entered in the second step. The regression statistics for the median RT and search accuracy are reported in F = 25.01, p < .001, and accounted for 18.5% of the variation of the differences whereas individualism and collectivism did not significantly predict the differences, F = 0.07, p = .992. Another hierarchical regression analysis revealed that race contributed significantly to the differences in the search time between FF and UF, F = 26.81, p < .001, and accounted for 19.6% of the variation in the difference between the search times whereas individualism and collectivism did not significantly predict the differences, F = 0.08, p = .990. Finally, neither race, F = 0.03, p = .868, nor the levels of individualism and collectivism of participants, F = 0.27, p = .900, contributed significantly to the differences between the search time of SF and FF.The hierarchical regression analysis revealed that race had contributed significantly to the differences in the search time between SF and UF, F = 9.47, p = .003, and accounted for 7.9% of the variation of the differences whereas individualism and collectivism did not significantly predict the differences, F = 0.81, p = .524. Another hierarchical regression analysis revealed that race contributed significantly to the differences in the search accuracy between FF and UF, F = 8.14, p = .005, and accounted for 6.9% of the variation in the search accuracy of the differences whereas individualism and collectivism did not significantly predict the differences, F = 0.88, p = .480. Finally, neither race, F = 0.90, p = .346, nor the level of individualism and collectivism of participants, F = 1.49, p = .209, contributed significantly to the differences between the search accuracy of SF and FF.The hierarchical regression analysis revealed that race had contributed significantly to the differences in the search accuracy between SF and UF, We expected that, regardless of the race of the participants, individuals with a higher score on both HI and VI would show a stronger SFA whereas individuals with a higher score on both HC and VC would show a weaker SFA. Contradicting the hypotheses, these results suggest that, for both the search time and search accuracy, the reported SFA relative to UF can be explained by participants’ race but not by participants’ self-construal as measured by the level of individualism and collectivism in the HCIV questionnaire.The aim of the current study was to identify the differences in the cultural background as a modulating factor of the SFA effect. We hypothesised that the SFA would be modulated by the cultural differences in the self-concepts of participants. Specifically, due to the cultural differences in the emphasis on the independent and interdependent self, we predicted that the SFA effect relative to a friend’s face would be larger in British Caucasians compared with Malaysian Chinese participants  whereas both race groups would show a comparable SFA effect relative to an unfamiliar face .Findings from this study showed that British Caucasian participants searched more accurately and faster for all faces, regardless of the face identity, compared with Malaysian Chinese participants. In addition, across both race groups, there were no differences in the search accuracy and search time of the own face and friend’s face whereas lower accuracy and longer search times were reported for unfamiliar faces than for those two types of familiar faces. In other words, the SFA effect was absent when the own face was compared to a friend’s face but present when compared with an unfamiliar face, and this finding was observed in both British Caucasian and Malaysian Chinese participants.Overall, these findings seemed to suggest that (1) one’s own face does not receive preferential processing when compared with another overlearned face ; (2) both familiar faces showed a processing advantage compared with unfamiliar faces; and (3) the absence of a SFA effect relative to a friend’s face is not modulated by the cultural differences in the self-concepts of participants, not at least in a visual-search paradigm.On the other hand, our results are in line with the findings of selfies. More importantly, photographs and videos offer a poor visual experience about the self-face. In fact, a large amount of research has shown that self-face representations are built through the combination of multisensory information, such as visual, tactile, and proprioceptive  due to frequent exposure to one’s own image through the mirror and photos and an extensive experience with highly familiar individuals . LikewisAlthough our findings suggest that the SFA can be explained in terms of familiarity, other studies have reported evidence of an SFA even when compared with personally familiar faces e.g.,  and famoArguably, it is also conceivable that participants are likely inexperienced at searching for their own face, such that they are asked to search for a small grey-scaled image of their face among an array of distractor faces. In contrast, it is a much more familiar task for participants to search for faces of their close friends. In other words, individuals may be more accustomed to the task of picking out a familiar face in a crowd rather than identifying the own face among an array of different faces . LikewisHence, one may argue that the lack of SFA relative to a friend’s face in our study may be due to the type of task employed. We would, however, like to highlight that even though our findings showed that there were no differences in the search performance between the own face and friend’s face, there is an advantage in the search time and search accuracy for familiar faces  compared with unfamiliar faces. Rather than attributing the lack of SFA relative to a friend’s face to the type of task employed, our findings are in line with the hypothesis that familiar faces are processed faster and more accurately due to a more robust mental representation and further reinforced the proposition that the own face might just be another highly familiar face.Contradicting our hypothesis and findings from previous studies e.g., , our finWe infer that the advantage in the search time for the self-face in British Caucasians compared with Malaysian Chinese cannot be accounted by the cultural differences in self-construal for two reasons. First, our findings showed that British Caucasian participants were overall more accurate and faster than Malaysian Chinese participants when searching for faces, regardless of the identity. Second, our findings from the regression analyses further indicated that the SFA (in terms of search accuracy and search time) relative to an unfamiliar face could not be explained by the cultural differences in self-construal  of the participants. Race of participants, however, could account for the variability in the search accuracy and search time between the self-face and the unfamiliar face and between the friend’s face and the unfamiliar face. Although previous research has shown cultural differences in the visual search of simply patterns between East Asian and British Caucasian participants , with thIn conclusion, the findings showed in a visual-search paradigm that although there is an SFA relative to unfamiliar faces across both British Caucasian and Malaysian Chinese participants, an SFA was not reported when compared with a friend’s face, and these findings are not modulated by the cultural differences in one’s self-concept. Taken together, this work seemed to suggest that, on a behavioural level at least, the observed SFA is better explained by a familiar face advantage rather than a processing advantage for the self-face. In other words, because we encounter our own face and friend’s face often, we have more robust mental representations of our own face and the face of our friend in comparison to unfamiliar faces whose mental representations are less robust. However, we do not have a more robust representation of our own face when compared with the representative of a friend’s face.


KRAS peptide nucleic acid clamp‐PCR, we measured ctDNA levels in plasma samples obtained at baseline and every 4 weeks during chemotherapy from 81 patients with locally advanced and metastatic PDAC. Cox proportional hazard regression showed that ctDNA detection at baseline was an independent predictor of progression‐free and overall survival. Joint modelling demonstrated that the dynamic ctDNA level was a strong predictor of time to first disease progression. Longitudinal ctDNA measurements during chemotherapy successfully revealed disease progression in 20 (67%) of 30 patients with ctDNA detected at baseline, with a median lead time of 23 days (P = 0.01) over radiological imaging. Here, we confirmed the clinical relevance of ctDNA in advanced PDAC with regard to both the prediction of clinical outcome and disease monitoring during treatment.Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a need for better tools to guide treatment selection and follow‐up. The aim of this prospective study was to investigate the prognostic value and treatment monitoring potential of longitudinal circulating tumour DNA (ctDNA) measurements in patients with advanced PDAC undergoing palliative chemotherapy. Using  KRAS peptide nucleic acid clamp‐PCR. The results demonstrated that ctDNA measurements at baseline predict clinical outcome and that serial ctDNA measurements can be used for monitoring treatment response.The authors collected 425 serial plasma samples from 81 patients with advanced pancreatic ductal adenocarcinoma. The level of circulating tumour DNA (ctDNA) was measured using  CA19‐9cancer antigen 19‐9cfDNAcell‐free DNACHIPclonal haematopoiesis of indeterminate potentialCIconfidence intervalsCqcycle quantificationCTcomputed tomographyctDNAcirculating tumour DNAddCqdelta delta cycle quantificationECOGEastern Cooperative Oncology GroupFOLFIRINOX5‐fluorouracil, irinotecan and oxaliplatinHRhazard ratioHUHHaukeland University HospitalKRASKirsten rat sarcoma virusnab‐paclitaxelnanoparticle albumin‐bound paclitaxelOSoverall survivalPBMCperipheral blood mononuclear cellsPDACpancreatic ductal adenocarcinomaPFSprogression‐free survivalPNApeptide nucleic acidRECISTresponse evaluation criteria in solid tumoursSUHStavanger University Hospital1Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a mortality rate close to the incidence rate and is predicted to be the second leading cause of cancer‐related death in the US in 2030 . SurgeryComputed tomography (CT) complemented by measurement of serum CA19‐9 is routinely used for monitoring of treatment response. Although imaging is considered the gold standard for tumour assessment, the dense fibrotic stroma in PDAC complicates the interpretation of CT‐scans, and challenges exist concerning intra‐ and inter‐observer variability even when using the standardised RECIST criteria , 5. MultCirculating tumour DNA (ctDNA) is an emerging tool that may be a useful supplement to traditional biopsies, imaging, and CA19‐9 in the diagnosis, prognostic stratification, and treatment evaluation of PDAC . ctDNA iKRAS ) in DNA from the Caco‐2  colorectal carcinoma cell line (KRAS wt). For the negative control, we used cfDNA from a healthy individual. To define a cut‐off for plasma KRAS mutation status, we analysed plasma samples from 29 healthy individuals. The lowest ΔCq was 11.27 . To enhance the interpretability of our KRAS mutation measurements, we computed ΔΔCq values, which were calculated as ΔΔCq = ΔCqwt,min − ΔCqsample. Samples with ΔΔCq > 0 were considered positive for KRAS‐mutated ctDNA, and ΔΔCq values < 0 were set to 0 in the statistical analyses. If a sample had only one replicate with amplification when the PNA clamp was present, this replicate was used in further calculations. If a sample had no amplification signal in both replicates with a PNA clamp, ctDNA was considered as being not detected. For patients with KRAS mutations detected in any plasma sample, DNA from PBMCs was analysed to ensure that the mutations were not due to clonal haematopoiesis of indeterminate potential (CHIP). Cell lines have been authenticated in the last 3 years using short tandem repeat profiling. All cell‐line experiments were performed with mycoplasma‐free cells.eviously  using an2.4P‐values for potential prognostic factors  and OS. PFS was defined as the time from signed consent until first radiological progression according to the RECIST 1.1 criteria  or deathr (version 4.1.2). Due to the endogenous nature of ctDNA levels, standard survival analysis with time‐varying covariates may give biased results [jmbayes2, in which the estimated current value of the ctDNA level was linked to time to first progression. Other functional forms of the association were explored . The estimated effect of ctDNA level on time to first progression is presented as a hazard ratio (HR) with accompanying 95% CI. Model predictions for a selection of patients representing typical longitudinal trajectories of ctDNA are presented in plots.Dynamic prediction of progression based on longitudinal measures of ctDNA levels were assessed using joint models in  results . First, Similar joint modelling was performed for CA19‐9 levels as a predictor of time to first progression. Due to an extremely skewed distribution, we used log2‐transformed CA19‐9 levels in the longitudinal model, and did not adjust for time since the last infusion. Supplementary analyses were done excluding the HUH patients.U‐test. Lead times for detection of disease progression were compared to zero time difference using Wilcoxon signed rank test with continuity correction. Patient characteristics were presented as counts and percentages and compared between groups using Fisher's exact test. P‐values ≤ 0.05 were considered significant. All statistical analyses were carried out using spss version 26.0 and r version 4.1.2 and 4.2.1.ctDNA levels at baseline, after 1 and 2 months of therapy, and at time of progression were compared using the Mann–Whitney 33.1KRAS gene (codon 12/13) as ctDNA marker. Baseline patient characteristics are summarised in Table KRAS mutations were detected in 44 of 79 (56%) plasma samples at baseline, in 42 of 62 (63%) samples from patients with metastatic disease, and in two of 12 (17%) samples from patients with locally advanced disease (P = 0.004). Sex, primary tumour location and differentiation grade, clinical M stage, liver metastases, and ECOG status were significantly associated with baseline ctDNA detection  patients had died. Kaplan–Meier analyses of ctDNA detected versus not detected at baseline demonstrated a median PFS of 3.3 (95% CI 1.4–5.1) months versus 7.0 (4.9–9.1) months . In a multivariable model (Table P = 0.010) and OS . Other independent predictors of PFS and OS were age, ECOG performance status, and first‐line treatment. Furthermore, liver metastasis was an independent prognostic factor for PFS.In univariable Cox regression, both ctDNA level and detection (yes/no) at baseline and after 1 month of chemotherapy were prognostic factors for PFS and OS Table . ECOG pe3.3P < 0.001). Similarly, 77 patients had at least one CA19‐9 measurement , and for these the HR of log2 CA19‐9 levels with regard to time to first progression was estimated to be 1.02 . The HR for the area under the trajectory of ctDNA levels was similar to the HR for the current value . For log2 transformed CA19‐9, the HR for slope (per 0.01 units) was estimated to be 1.33  and for area 1.02 . Prediction plots for time to first progression based on the joint model in Table We measured ctDNA levels in blood samples obtained monthly during chemotherapy from the included patients. Eighty‐one patients had at least one ctDNA measurement and were included in the joint model, with a total of 298 observations. The complete joint model is presented in Table P < 0.001; based on 63 patients, 245 observations). For the slope of log2 CA‐19‐9 levels (per 0.01 units) it was 1.24 .Excluding the smallest site (HUH), the corresponding HR for ctDNA level was 1.23  had either persistently high ctDNA levels (n = 3) or initially decreasing ctDNA levels that increased again at the time of or earlier than radiological disease progression (n = 17), whereas 10 patients had decreasing ctDNA levels that did not reflect disease progression . The median lead time of ctDNA‐based detection of disease progression over radiological detection was 23 days . We performed a similar analysis of CA19‐9 and found a median lead time of 9.5 days (P = 0.03) for the 18  patients who had CA19‐9 increase (> 50%) at or before the time of radiological evidence of progression , emphasising the importance of the amount and quality of input cfDNA. Biological aspects also contribute to a low detection rate, as PDAC seems to generally shed less ctDNA than other cancer types [Despite approximately 90% of PDACs having mutations in KRAS , 33, we ced PDAC , 20, 30.2 and 13 . Overall2 and 13  and resu2 and 13 . In the er types , 36. Higer types , 39, 40.We demonstrated an independent prognostic value of baseline ctDNA detection in our study, an observation that confirms previously published results, including several meta‐analyses , 14. OurWe observed somewhat shorter progression‐free and overall survival among the patients recruited at the main study site (SUH) compared to the secondary site (HUH). This seemed to be related to the lower baseline ECOG performance and the more frequent treatment with the FOLFIRINOX combination at the HUH site. The latter could partly be because the recruitment of patients started later at the secondary site, when FOLFIRINOX was recommended. With regard to the validity of our conclusions, the number of patients with ctDNA detected at baseline was not significantly different between the sites and the prognostic relevance of ctDNA was verified also when excluding the samples from HUH.Longitudinal analysis of ctDNA levels demonstrated that ctDNA monitoring revealed disease progressions at the same time or earlier than radiological monitoring in 20 of 30 (67%) patients with detectable ctDNA levels at baseline Fig. , with a 5In the current study, we confirmed the clinical relevance of ctDNA in advanced PDAC with regard to prediction of outcome and disease monitoring during chemotherapy. To establish the clinical utility of ctDNA in this context, there is a need for prospective interventional studies exploring the survival and quality of life benefits of treatment choices based on ctDNA measurements. Apparently, lower levels of ctDNA in some patients may reduce its utility in monitoring PDAC, but this may be compensated for by applying multiple ctDNA marker types and larger sample volumes. ctDNA monitoring may also be more useful in specific clinical contexts, especially with regard to potential future biologically targeted therapies.The authors declare no conflict of interest.KHE collected clinical data, performed laboratory and statistical analyses, and drafted the manuscript. KT participated in the study design, laboratory analyses, data interpretation and manuscript preparation. ML participated in data analysis, interpretation and manuscript preparation. SO performed laboratory analyses, data interpretation and reviewed the manuscript. ID supervised and performed statistical analyses and prepared the manuscript. HG and NG contributed to patient recruitment, data interpretation and manuscript preparation. BG contributed to the study design and coordination, data interpretation and manuscript preparation. ON contributed to the study design and coordination, statistical analyses, data interpretation, and manuscript preparation. All authors read and approved the final version of the manuscript.https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/1878-0261.13472.The peer review history for this article is available at Animation S1. Dynamic prediction of first progression for a 72‐year‐old woman from Stavanger University SUH with liver metastases, ECOG = 0, first‐line medication FOLFIRINOX, time of first progression 284 days.Click here for additional data file.Animation S2. Dynamic prediction of first progression for a 51‐year‐old woman from SUH with liver metastases, ECOG = 1, first‐line medication nab‐paclitaxel/gemcitabine, time of first progression 162 days.Click here for additional data file.Animation S3. Dynamic prediction of first progression for a 72‐year‐old woman from SUH with liver metastases, ECOG = 1, first‐line medication nab‐paclitaxel/gemcitabine, time of first progression 173 days.Click here for additional data file.Animation S4. Dynamic prediction of first progression for a 52‐year‐old man from HUH with liver metastases, ECOG = 1, first‐line medication FOLFIRINOX, time of first progression.Click here for additional data file.Fig. S1. Plasma ctDNA, serum CA19‐9 levels and radiological monitoring data for selected patients.Click here for additional data file.Table S1. Joint modelling of longitudinal CA19‐9 and time to first progression.Click here for additional data file.


Previous studies have found the potential role of gout or hyperuricemia in subsequent development of Alzheimer's disease (AD) but reported inconsistent results. We conducted the current meta‐analysis to evaluate whether an association exists between gout/ hyperuricemia and AD.We systematically searched PubMed and EMBASE for the published cohort studies that measured the risk of AD in subject with gout/ hyperuricemia up to May 20, 2023. Data extraction was employed by two authors independently. Rev Man 5.3 and Stata 15.0 software were used to calculate the relative ratio (RR) or hazard ratio (HR) for including studies. Subgroup analysis was performed to assess the sources of heterogeneity. A random‐effects model was adopted when heterogeneity was present. The funnel plot, Begg's test, and and Egger's test were used to assess publication bias.After rigorous screening, seven eligible studies were included in the final analyses. Pooled results indicated that gout or hyperuricemia decreases the risk of AD , with a high heterogeneity of 93%. Subgroup analyses showed that regional distribution was the source of heterogeneity. Egger's and Begg's tests as well as visual inspection of funnel plot suggested no publication bias in the studies.The findings suggested that gout or hyperuricemia might have a protective effect against AD. This negative correlation should be verified by more cohort studies due to the existence of substantial heterogeneity. In the current meta‐analysis, we comprehensively evaluated the association between gout/hyperuricemia and AD based on evidence from high‐quality cohort studies. Interestingly, the results showed a 31% AD risk reduction for gout or hyperuricemia. This indicated that gout or hyperuricemia might be a protective factor approach to prevent AD formation.  Existing epidemiological surveys indicated that approximately 50 million people worldwide were living with dementia, projected to triple in 2050  crystals in the synovial fluid of articular and peri‐articular tissues, resulting in gouty arthropathy, tophi formation and organ damage Lee, . Gout isBecause of the inconsistencies among studies, we conducted an up‐to‐date cohort based meta‐analysis to quantify the correlation of gout or hyperuricemia with AD.2These systematic review and meta‐analysis were conducted in accordance with the preferred reporting items for systematic reviews and meta‐analysis (PRISMA) guidelines  independently searched the PubMed and EMBASE for papers published from their inceptions to May 20, 2023. Medical Subject Headings (MESH) terms set in the search were (“gout” OR “arthrolithiasis” OR “podagra” OR “dominus merborum” OR “hyperuricaemia”) AND (“Dementia” OR “Alzheimer's disease” OR “Mixed Dementia” OR “Cognitive decline” OR “Cognitive impairment”) NOT .2.2Articles were included if they fulfilled the following inclusion criteria: (1) prospective or retrospective cohort studies; (2) the exposed group consisted of gout or hyperuricemia patients, while controlled group consisted of AD patients; (3) report of risk estimates [hazard ratios (HR) or rate ratios (RR)] with 95% confidence intervals (CIs) as the outcome; (4) studies without restrictions by language of publication. Additionally, reviews, case‐control or cross‐sectional studies, and publications without adequate data were excluded.2.3The following data were extracted independently by two authors (ZT and F‐YW): first author, publication year, geographic location (country), study design, sample size, male ratio, mean or range of age among patients, diagnostic criteria of gout/ hyperuricemia and dementia, duration of follow‐up, outcome, adjustment, and study quality . The Newcastle‐Ottawa Scale (NOS) Stang,  was appl2.4Q and I2 statistic; I2 <50%, 50%∼75%, and > 75% indicated low, medium, and high homogeneity, respectively  from each study to evaluate the relationship between LOE and risk of dementia. Heterogeneity of HR or RR across articles was assessed using the Cochrane's 33.1As shown in Figure 3.2The details of the included studies were shown in Table 3.3I2 = 93%, p < .001).Pooled results showed that individuals presenting with gout or hyperuricemia had a high probability of reducing AD . Neither subgroup analysis by study design nor sample size could explain the origin of the higher heterogeneity. However, when using regional distribution as subgroup, the Asia studies showed lower heterogeneity  and Egger's test  did not reveal potential publication bias.On visual inspection, the funnel plot seemed to be asymmetrical Figure . However4In the current meta‐analysis, we comprehensively evaluated the association between gout/hyperuricemia and AD based on evidence from high‐quality cohort studies. Interestingly, the results showed a 31% AD risk reduction for gout or hyperuricemia. This indicated that gout or hyperuricemia might be a protective factor approach to prevent AD formation. Additionally, these relationships remained consistent across subgroup and sensitivity analyses.Although a previous meta‐analysis investigated the association between gout/hyperuricemia and dementia, the results suggested no statistically significant risk of all‐cause dementia in patients with gout or hyperuricemia  may be more helpful in exposing the cognitive effects of high uric acid (Euser et al., For the subgroup analysis, the source of heterogeneity may be related to regional distribution. Therefore, more studies from different regions are needed to clarify the connection of gout or hyperuricemia with risk of AD.Some limitations should be addressed. First, as mentioned above, there are the limited studies that examine the effect of gout or hyperuricemia on AD, thereby reducing the accuracy of the pooled estimates. Additionally, it is not possible to distinguish the different effects of gout on various types of dementia. Second, inconsistencies in the clinical diagnostic criteria for gout, hyperuricemia and AD across studies may affect the stability of outcomes. Third, previous studies have shown that the use of antigout preparations, with particular reference to benzbromarone, was negatively correlated with dementia (Taipale et al., In summary, our meta‐analysis suggested negative relationship of gout or hyperuricemia with risk of AD. This finding supports the protective effect of gout or hyperuricemia on cognitive function. More basic and clinical evidence from future studies needs to be clarified the cause‐and‐effect association between gout and AD.J‐CW were responsible for study design, overall planning, and project administration. LW and ZT contributed to the data curation. ZT, F‐YW, and W‐PW performed investigation, methodology, resources, and validation. LW and ZT drafted the original manuscript. ZT contributed to statistical analysis and interpreted the results. All authors have read and approved the submission.The authors declare no conflict of interest in this paper.https://publons.com/publon/10.1002/brb3.3207.The peer review history for this article is available at


The clinicopathological features of MET‐amplified gastric cancer (GC) and real‐world data on the efficacy of MET‐targeted therapies remain unknown. Pulmonary lymphangitis carcinomatosis (PLC) is a peculiar manifestation of GC, whose management has not been thoroughly described.This study analyzed patients diagnosed with MET‐amplified GC or GC with PLC at any time point of the disease course from 2011 to 2021 in two centers. Clinicopathological features and survival outcomes of MET‐amplified GC were analyzed. The clinical and molecular implications of GC with PLC were discussed.p = 0.010), diagnosed at a younger age (p = 0.002), presented with a higher baseline ECOG PS (p = 0.016), and was more likely to develop lung metastasis (p < 0.001), and serous effusion (p = 0.026) than GC without PLC. Patients with primary MET‐amplified GC had a worse prognosis than those with secondary MET‐amplified GC (p = 0.005). The application of anti‐MET therapy was associated with numerically prolonged survival, but the association was not statistically significant (p = 0.07). MET amplification was concentrated in patients with PLC, in which anti‐MET therapies elicited a high response rate.Fifty‐eight patients with MET‐amplified GC and 20 patients with GC accompanied by PLC were finally enrolled for analysis (including 13 overlapped patients). GC with PLC was more common in female patients (MET‐targeted therapies are efficacious in real‐world populations with MET‐amplified GC. Patients with PLC have distinct clinical and molecular features and might benefit from MET‐targeted therapies. Previous studies have indicated that breast, gastric, and lung cancers are the most common malignancies that give rise to PLC, while the pathogenic mechanisms of PLC are not understood.We sought to investigate the clinicopathological and prognostic features of MET‐amplified GC. Our study also revealed that MET amplification is a molecular event enriched in GC with PLC, which represents a distinct subtype showing responses to molecular targeted therapies.22.1In our two‐center retrospective study, eligible patients were diagnosed with recurrent or metastatic GC that was either MET‐amplified or manifested with PLC at any time point of the disease course from 2011 to 2021 in two medical centers. Tumor samples were generated from all patients by endoscopy, surgery, or needle biopsy at primary and/or secondary tumor sites. Tissues were fixed with formalin and paraffin‐embedded in blocks, which were prepared at the time of biopsy. Patients with available baseline characteristics and clinical data were included in the analysis. All patients were followed until October 2021.2.2MET gene copy numbers in all tumor samples were detected by next‐generation sequencing (NGS). NGS analysis was conducted by Genecast, Inc., a College of American Pathologists (CAP)‐accredited laboratory. A customized 769‐gene panel (Genecast) was employed to detect point mutations, indels, fusions, rearrangements, and CNVs. Targeted sequencing was performed on the NovaSeq 6000 platform (Illumina). The cutoff for MET amplification was 2.0 times . The PLC was determined by two independent radiologists based on chest computed tomography (CT) scans. Typical radiological findings include smooth or nodular thickening of interlobular septa, peribronchovascular interstitium, polygonal arcades with thickened limbs, and ground glass opacities. Clinical manifestations  also assisted in the diagnosis.2.3HER2, programmed death ligand 1 (PD‐L1), and mismatch repair (MMR) status were evaluated through IHC. HER2 expression was analyzed using the anti‐HER‐2 (4B5) rabbit monoclonal primary antibody . HER2 positivity was defined as IHC 3+ or both IHC 2+ and FISH positive. IHC of MLH1, MSH2, MSH6, and PMS2 was performed with a BenchMark ULTRA slide processing system  according to the manufacturer's instructions. Tumors were defined as deficient mismatch repair (dMMR) only if at least one of the proteins was absent of staining in the tumor cells; otherwise, proficient mismatch repair (pMMR) was deemed. PD‐L1 staining was performed using a 22C3 pharmDx kit . A combined positive score  of 1 or higher was considered PD‐L1 positive.2.4HER2 amplification was detected by fluorescence in situ hybridization (FISH) using a PathVysion HER‐2 DNA probe kit . Cases with a HER2:centromere 17 ratio >2.0 were considered FISH positive. The presence of EBV infection was determined by ISH using probes against Epstein–Barr virus‐encoded RNAs (EBER); (Leica Biosystems). Specimens with dark blue staining in tumor cell nuclei were deemed EBV‐positive.2.5p < 0.05.Statistical analyses were performed using SPSS software  and Prism . Categorical variables were evaluated using the chi‐square test or Fisher's exact test, as appropriate. Survival curves were plotted using the Kaplan–Meier method, and survival rates were compared between groups using the log‐rank test. Cox multivariate regression analyses were used to determine factors that affect survival outcomes. Overall survival (OS) was calculated from the time of diagnosis until death or the last observation for surviving patients. Progression‐free survival (PFS) was defined as the interval from the start of therapy to treatment discontinuation for disease progression or death of any cause. All tests were two‐sided, and differences were considered significant when 33.1n = 58, accounted for 6.2% of the total patients), the median age at diagnosis was 53 years , with a male preponderance. Twenty‐eight out of 58 (48.3%) patients had serous effusion. Liver, bone, and lung metastasis occurred in 21 (36.2%), 15 (25.9%), and 21 (36.2%) patients, respectively. GC with PLC was more common in females , diagnosed at a younger age , and presented with a higher baseline ECOG PS  than GC without PLC. Lung metastasis and serous effusion were more frequent in GC with PLC than in GC without PLC . GC with PLC accounted for a higher percentage of bone metastasis and poorly differentiated histology  than GC without PLC, though the differences were not significant. For molecular features, the average MET gene copy number was 7.4 . No difference was found in MET gene copy number according to PLC . One out of 13 GC with PLC and 6 out of 45 GC without PLC were HER2 positive. All patients with detectable pathological specimens had pMMR and EBV‐negative tumors. Thirty‐nine percent of patients (15/38) had PD‐L1‐positive GC, with CPS ranging from 1 to 98.A total of 937 patients diagnosed with stage IV GC were included for eligibility screening. Of these, 65 patients were eligible and had available clinicopathological information. Fifty‐eight patients with MET gene‐amplified GC and 20 patients with GC accompanied by PLC were finally enrolled for analysis (including 13 overlapped patients). A flow diagram is shown in Figure 3.2p = 0.038; p = 0.029; p = 0.016, respectively).  received ≥3 lines of therapies. Twenty‐eight patients received anti‐MET TKIs, of which 13 (46.3%) achieved PR, 4 (14.3%) achieved SD, and 3 (10.7%) achieved non‐CR non‐PD for the best response. The median duration of response was 3.0 months . Most of the patients received anti‐MET therapies as first‐ or second‐line treatment (20/28). In our cohort, patients with PLC received more lines of therapies, with 68.8% (11/16) undergoing ≥3 lines of therapies. In addition, patients with MET‐amplified GC and PLC underwent more intensive treatments, including a higher frequency of anti‐MET therapies, antiangiogenic therapies, and immunotherapies (3.3p = 0.005); ;  were added into the multivariate model. Only secondary MET amplification was a predictive factor of a prolonged OS with statistical significance .In Cox regression analysis, age, gender, and prognostic factors with 3.4A total of 20 patients were diagnosed with PLC and included for analysis. The median age at diagnosis was 42.5 years (range 23–77 years). Eleven out of 20 patients were female. Ten patients had an ECOG PS of 0–1, and the other 10 patients had an ECOG PS of 2–3. Seventeen patients had tumors with assessable MET gene status, of which 13 (76.5%) were confirmed MET‐amplified and otherwise nonamplified. The genomic variation information of 17 patients with PLC is shown in Figure Eleven patients received ≥3 lines of treatment for PLC. Ten patients received anti‐MET therapies. Regarding drug types, 10 patients received anti‐MET therapy with crizotinib, one of whom also received volitinib. Owing to the absence of measurable lesions in a few cases, therapeutic responses were determined from two perspectives . Fourteen patients rapidly alleviated their symptoms  or obtained radiographical remission after systemic therapies. Figure 3.5Temporal and spatial heterogeneity were found in several patients who progressed on anti‐MET therapies Figure . MET cop4MET‐amplified GC is a rare tumor subtype that has shown sensitivity to anti‐MET TKIs in small‐scale studies. However, the reported response rates are diverse. In a phase 2 single‐arm trial, second‐line AMG‐337  resulted in an overall response rate (ORR) of 18% in patients with pretreated MET‐amplified GC, with a median PFS of 3.4 months.We report for the first time the clinicopathological and molecular features of MET‐amplified GC. The absence of an internationally recognized set of standards has led to the investigation of various cutoff values to define MET amplification. We took a cutoff of 2.0  in our study. Our previous research used this cutoff, which mirrored the HER2 amplification cutoff.Importantly, in this study, we identified that PLC is a unique manifestation of GC. According to past literature, PLC denotes an end‐stage condition of malignancy characterized by the infiltration of lymphatic vessels by tumor cells. GC with PLC is a rare condition, with only a few cases reported, and the disease characteristics or management have not been extensively investigated.It is noteworthy that GC with PLC accounts for a considerable proportion of MET‐amplified GC. In our cohort, 16/58 (27.6%) patients developed PLC at any point during the disease course. Among 17 patients with GC and PLC who had assessable MET status, 13 (76.5%) were confirmed to be MET‐amplified, indicating that PLC is enriched in MET‐amplified tumors. It is reasonable to infer that aberrant MET signaling potentially gives rise to PLC, which confers profound clinical implications. In this regard, a patient with PLC should be suspected of having a MET‐amplified tumor and be referred for genetic testing. Conversely, clinicians should be alert to PLC during the subsequent course of MET‐amplified GC for earlier interventions Figure . Given tIn our study, we also revealed that systemic therapies often lead to focal control of lung lesions. For patients with MET‐amplified GC, anti‐MET TKIs are remarkably efficient, indicated by target lesion shrinkage and/or rapid symptom relief. A vast number of patients with PLC do not have antitumour opportunities due to a poor physical state . Nevertheless, once PLC is controlled, patients might regain the opportunity for systemic therapies to diminish tumor burden, therefore improving long‐term survival. In that case, timely molecular diagnosis is crucial. Considering the poor physical condition of patients with PLC, reexamination of tumor tissue is impractical. Liquid biopsy  might be an ideal alternative due to its low invasiveness.We also shed light on the heterogeneity of MET status in GC. MET aberrations could coexist with other driver molecular changes simultaneously, such as FGFR or HER2 amplification. Previous literature has demonstrated inconsistencies in molecular characteristics between primary and metastatic lesions.Our study has several limitations. First, the small‐scale and retrospective nature of this study could be an inherent defect. We acknowledge that it is difficult to interpret the survival data due to the limited number of enrolled patients and the variations in treatments. Further validation is warranted to consolidate the relationship between PLC and MET amplification, although the overall rarity and aggressive nature of GC with PLC make prospective studies challenging. Second, each detection method of MET amplification has its flaws. NGS is less sensitive than FISH, which might affect the detection rate of MET amplification. Third, in our cohort, PLC was diagnosed via CT and clinical manifestations, which might lead to underdiagnosis of very mild cases .5Our study indicates that MET amplification could be enriched in gastric cancer accompanied by pulmonary lymphangitis carcinomatosis, and the latter shows a high response rate to anti‐MET target therapies.Zhening Zhang: Conceptualization ; formal analysis ; investigation ; writing – original draft . Yiyi Yu: Conceptualization ; data curation . Tong Xie: Resources . Changsong Qi: Resources . Xiaotian Zhang: Resources . Lin Shen: Funding acquisition ; writing – review and editing . Zhi Peng: Conceptualization ; funding acquisition ; writing – review and editing .This work was supported by National Natural Science Foundation of China (81602057); Beijing Natural Science Foundation (Z210015); Clinical Medicine Plus X‐Young Scholars Project of Peking University  and the Fundamental Research Funds for the Central Universities.The authors declare that they have no competing interests.The present study was approved by the ethics committee of Peking University Cancer Hospital  and Fudan University Zhongshan Hospital , and conducted in accordance with the Declaration of Helsinki. Written informed consent for the participation of this retrospective study was exempted by the ethics committee of Peking University Cancer Hospital and Fudan University Zhongshan Hospital.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.Figure S5.Click here for additional data file.Table S1.Click here for additional data file.


Secondhand smoke (SHS) exposure causes various health problems associated with an unhealthy lifestyle. However, the lifestyles of individuals exposed to SHS have not been characterized extensively. Therefore, this cross-sectional study aimed to investigate the association between SHS exposure and lifestyle behaviors. The participants were 2379 healthy male employees at Osaka University who underwent health examinations. Physical and biochemical parameters and lifestyle behavior data were obtained from all the participants. Participants with SHS exposure had significantly higher body mass index, waist circumference, and serum levels of triglycerides and uric acid than that of those without SHS exposure. SHS exposure was significantly correlated with several lifestyle behaviors, including TV time, frequency of breakfast consumption and fried food consumption, vegetable and fruit intake, alcohol consumption frequency and daily alcohol intake, and smoking status. Thus, SHS exposure may be associated with an unhealthy lifestyle. The lifestyle behaviors of the smoke-excluded population were assessed further; however, SHS exposure was still associated with dietary and drinking habits. Since participants with SHS exposure are likely to have an unhealthy life and combined unhealthy lifestyle behaviors, the confounding effect of these factors should be considered when assessing the impact of SHS exposure on health. SHS can cause various health problems including cardiovascular diseases, lung cancer, breast cancer, chronic obstructive pulmonary disease, sudden infant death syndrome, and depression8.The harmful effects of smoking on health are not limited to smokers and extends to those exposed to secondhand smoke (SHS). SHS contains hundreds of toxic chemicals and approximately 70 carcinogens. Thus, SHS exposure leads to approximately 880,000 deaths every year, globally9. Among the NCDs risk factors, lifestyle behaviors including smoking habits, physical inactivity, unhealthy diet, and excessive alcohol consumption, are considered extremely important based on their modifiability10. In addition to deaths caused by smoking, previous studies report that physical inactivity causes 3 million deaths, unhealthy diet causes 14 million deaths, and excessive alcohol consumption causes 2.3 million deaths each year10. Therefore, assessing and controlling lifestyle behaviors are necessary for NCD prevention and management.Like smoking, unhealthy lifestyles are associated with chronic diseases, known as non-communicable diseases (NCDs). More than 70% of all deaths globally are caused by NCDs including cancers, cardiovascular diseases, chronic respiratory diseases, and diabetes, accounting for 80% of NCD deaths11. As combined unhealthy lifestyle behaviors are more harmful to health than a single unhealthy lifestyle behavior12, detailed assessment of the lifestyle of individuals is necessary.Lifestyle behaviors of smoking and non-smoking populations have been reported to be different. Lifestyle in the smoking population was unhealthier than that in the non-smoking populationTo the best of our knowledge, the lifestyles of individuals exposed to SHS have not yet been evaluated extensively. Thus, in the present study, we evaluated the association of SHS exposure and lifestyle behaviors using cross-sectional data from male university workers.This study included cross-sectional data collected from male Osaka University employees who underwent an annual health examination at the Osaka University Health and Counseling Center. The exclusion criteria for this study were as follows: (1) individuals with underlying health conditions, (2) individuals who were administered long term or frequent medication for at least 1 year before their health examination, (3) individuals with acute illness within the previous 2 weeks of the examination, and (4) individuals who did not answered the questionnaire on SHS exposure. In total, 2379 Japanese males were enrolled in this study. This study was conducted in accordance with the Declaration of Helsinki and the Ethics Guidelines for Clinical Research of the Ministry of Health, Labour and Welfare and the Ministry of Education, Culture, Sports, Science, and Technology. All experimental protocols in this study were approved by the Ethics Committee of the Health and Counseling Center, Osaka University, and written informed consent was obtained from all participants prior to participation in the study.2]), waist circumference (WC) at the umbilical level, systolic blood pressure, and diastolic blood pressure were measured as physical parameters.Body mass index , creatinine (Cr), uric acid (UA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), fasting plasma glucose (FPG), and HbA1c were measured as biochemical parameters.Exercise frequency: “How many days a week do you exercise?” on a five-point scale: 1 = 5–7 days a week, 2 = 3–4 days a week, 3 = 2 days a week, 4 = 1 day a week, 5 = 0 days a week.Daily exercise duration: “How long do you exercise per day?” on a five-point scale: 1 =  > 120 min, 2 =  ≤ 120 min, 3 =  ≤ 60 min, 4 =  ≤ 30 min, 5 =  ≤ 10 min.Time watching TV or videos: “How long do you watch TV or videos per day?” on a three-point scale: 1 =  < 30 min, 2 = 30–120 min, 3 =  > 120 min.Breakfast consumption frequency: “How many days a week do you eat breakfast?” on a five-point scale: 1 = every day, 2 = 5–6 days a week, 3 = 3–4 days a week, 4 = 1–2 days a week, 5 = 0 days a week.Lunch consumption frequency: “How many days a week do you eat lunch?” on a five-point scale: 1 = every day, 2 = 5–6 days a week, 3 = 3–4 days a week, 4 = 1–2 days a week, 5 = 0 days a week.Dinner hour: “What time do you eat dinner on average?” on a four-point scale: 1 = by 7 PM, 2 = by 9 PM, 3 = by 11 PM, 4 = later than 11 PM.Fried food consumption frequency: “How many days a week do you eat fried food?” on a three-point scale: 1 = 0–2 days a week, 2 = 3–4 days a week, 3 = 5–7 days a week.Vegetable and fruit intake: “How many vegetables or fruits do you eat per day?” on a five-point scale: 1 = a large amount, 2 = above moderate, 3 = moderate amount, 4 = below moderate, 5 = a small amount.Alcohol consumption frequency: “How many days a week do you drink alcohol?” on a five-point scale: 1 = 0 days a week, 2 = 1–2 days a week, 3 = 3–4 days a week, 4 = 5–6 days a week, 5 = every day.Daily alcohol intake: “How much pure alcohol do you consume on a typical day when you are drinking?” on a three-point scale: 1 =  < 20 g, 2 = 20–40 g, 3 =  > 40 g.Smoking status: “What is your smoking status?” on a three-point scale: 1 = never smoker, 2 = former smoker, 3 = current smoker.Information on SHS exposure and lifestyle behaviors of the study participants was obtained using questionnaires. All responses were reconfirmed through expert interviews with the trained nurses. SHS exposure was ascertained by a question “Are you regularly exposed to SHS indoors at home or the workplace?” and the answer was semi-quantified as 0 = no and 1 = yes. Lifestyle behavior questionnaires regarding physical and leisure activities, dietary habits, drinking habits, and smoking habits were asked as indicated below. Each answer was semi-quantified using the following scales, with larger number indicating an unhealthy lifestyle.p < 0.05.All statistical analyses were performed using STATA 14 . The distribution of continuous variables was assessed using the Shapiro–Wilk test. The Mann–Whitney U test was used to compare the two groups. Kendall’s rank correlation coefficient and multiple regression analysis using backward elimination method (p ≥ 0.2 removed) were used to analyze the variables. The chi-square test was used to compare the proportions of categorical values between the two groups. Statistical significance was set at p = 0.013 and p = 0.017), and serum concentrations of UA and TG (p = 0.007 and p = 0.019) were significantly higher than that in the SHS exposure (−) participants. Serum Cr and HDL-C levels (p = 0.001 and p = 0.026) were significantly higher in the SHS exposure (−) participants than in the SHS exposure (+) participants.The characteristics of the participants are presented in Table p = 0.033), breakfast consumption frequency , fried food consumption frequency , vegetable and fruit intake , alcohol consumption frequency , daily alcohol intake , and smoking status . These positive correlations suggest that SHS exposure is associated with an unhealthy lifestyle. For further analysis of the relation between SHS exposure and each parameter, multiple regression analysis was performed as presented in Table p = 0.024), Cr , UA , TC , TG , FPG , breakfast consumption frequency , fried food consumption frequency , daily alcohol intake , and smoking status  were observed as the influence factors of SHS exposure. There was no multicollinearity among these parameters.Table p < 0.0001), fried food consumption frequency (p = 0.028), vegetable and fruit intake (p = 0.045), alcohol consumption frequency (p = 0.014), daily alcohol intake (p < 0.0001), and smoking status (p < 0.0001) between the two groups. Although long term TV viewing was correlated with SHS exposure, a significant difference was not observed between participants with and without SHS exposure (p = 0.094).Since SHS exposure was related to several lifestyle behavior parameters, we assessed the differences in lifestyle between SHS exposure (+) and SHS exposure (−) participants. A comparison of the proportion of participants who answered the lifestyle behavior questionnaires among SHS exposure (+) and SHS exposure (−) participants is presented in Fig. 11, the increased prevalence of current smokers might influence the lifestyle of participants with SHS exposure. Therefore, we further assessed lifestyle behaviors among the study participants, excluding current smokers. The characteristics of smoke-excluded population are presented in Supplementary Table p = 0.008), lunch consumption frequency , fried food consumption frequency , vegetable and fruit intake , and daily alcohol intake . Significant differences were also observed in the proportion of breakfast consumption frequency (p = 0.018), lunch consumption frequency (p = 0.043), fried food consumption frequency (p = 0.014), vegetable and fruit intake (p = 0.028), and daily alcohol intake (p < 0.0001) between participants with and without SHS exposure. Even after excluding the smoking population, SHS exposure was associated with dietary and drinking habits.Figure 13. In the present study, we examined physical, biochemical, and lifestyle behavioral parameters among participants with and without SHS exposure. SHS exposure (+) participants had significantly higher BMI and WC than SHS (−) participants. While exposure to SHS is reported to be associated with overweight/obesity in children, only a few studies have reported an association between SHS exposure and weight gain in adult17. Several mechanisms, including inflammation and oxidative stress, have been associated with SHS exposure and overweight/obesity19. In the SHS exposure (+) group, significantly increased TG and UA were also observed along with increased BMI. As TG and UA levels are associated with oxidative stress21, SHS exposure may have induced oxidative stress in the participants. However, these significant differences were not observed after excluding the smoking population  participants are unhealthy compared to those of SHS exposure (−) participants. Excessive alcohol consumption is a well-known risk factor for various health conditions and chronic diseases31. The alcohol consumption frequency and daily alcohol intake was significantly different between participants with and without SHS exposure. Besides the influence of oxidative stress from SHS exposure, sedentary behaviors, unhealthy diet, and excessive alcohol consumption might also cause an increase in TG and UA levels in participants with SHS exposure34. A comparison of the smoking status revealed that the prevalence of current smokers was high among SHS exposure (+) participants. Thus, participants exposed to SHS are likely to inhale both active and passive smoke, which is even more unhealthy. Moreover, we assessed the association between SHS exposure and lifestyle behaviors in the smoke-excluded population. Even though the lifestyle behaviors of the smoking population were excluded, we found that participants with SHS exposure were likely to have unhealthy dietary and drinking habits. From these lifestyle behavior assessments, this study suggests that participants with SHS exposure live an unhealthy life and have combined unhealthy lifestyle behaviors.This study revealed that SHS exposure was associated with several lifestyle behaviors, including TV time, diet, alcohol consumption, and smoking habits. TV viewing, considered a sedentary behavior, is the most common leisure activity. As a sedentary lifestyle is associated with health problems, prolonged TV viewing has been reported to be associated with all-cause mortality, type 2 diabetes, and cardiovascular diseases36. Hence, these confounding factors should be assessed to evaluate the risk of SHS exposure. A previous study reported that adherence to body weight, diet, alcohol intake, and physical activity recommendations was associated with a lower risk of mortality in former smokers37. As participants with SHS exposure have combined unhealthy lifestyle behaviors, preventing the exposure to SHS and promoting a healthy lifestyle based on multiple aspects would be essential for maintaining individual health and reducing the risk of health problems.Combined unhealthy lifestyle behaviors is a significant risk factor for all-cause mortality. A combination of unhealthy lifestyle behaviors shows a synergistic effect; therefore, an increased number of unhealthy lifestyle behaviors is associated with a higher risk of mortality39. Since self-reported SHS exposure might underestimate the intensity and frequency of SHS exposure, the association between SHS exposure based on cotinine levels and lifestyle behaviors should be assessed in future studies. Another limitation was that this was a single-center study with limited age range of subjects. At Osaka University, an indoor smoking ban on public places, except for several smoking rooms, has been implemented since 2004. Thus, the study participants might have been less exposed to SHS in the workplace compared to the participants in other institutions. Moreover, as the study participants were university employees, their background including age, educational level, and income might be different from other population. Since age and socioeconomic status have been reported to impact on lifestyle behaviors42, further study regarding different age range and socioeconomic status is needed to be performed.This study had some limitations. SHS exposure was self-reported by the participants. The serum or urine cotinine is an established marker for SHS exposureIn conclusion, we evaluated the association between SHS exposure and physical, biochemical, and lifestyle behavioral parameters in the present study. BMI, WC, serum TG, and UA levels were elevated in SHS exposure (+) participants. SHS exposure is associated with several lifestyle behaviors, including TV time, dietary habits, alcohol consumption, and smoking status. Moreover, participants with SHS exposure were more likely to have an unhealthy lifestyle than participants without SHS exposure. To the best of our knowledge, this is the first study to reveal lifestyle behaviors in participants exposed to SHS. While avoiding to expose SHS has been the main target for preventing the health problems caused by SHS exposure, the lifestyle behaviors among individuals exposed to SHS have not been discussed extensively. However, this study showed that participants with SHS exposure are likely to have an unhealthy life and combined unhealthy lifestyle behaviors. This combination of unhealthy lifestyle behaviors is expected to result in serious health problems. Therefore, comprehensive health promotion would be necessary to prevent NCDs and other health problems caused by SHS exposure.Supplementary Information.