Mild traumatic brain injuries (mTBIs) are the most common form of acquired brain injury. Symptoms of mTBI are thought to be associated with a neuropathological cascade, potentially involving the dysregulation of neurometabolites, lipids, and mitochondrial bioenergetics. Such alterations may play a role in the period of enhanced vulnerability that occurs after mTBI, such that a second mTBI will exacerbate neuropathology. However, it is unclear whether mTBI-induced alterations in neurometabolites and lipids that are involved in energy metabolism and other important cellular functions are exacerbated by repeat mTBI, and if such alterations are associated with mitochondrial dysfunction.in vivo proton magnetic resonance spectroscopy (1H-MRS), and mitochondrial bioenergetics were evaluated 30 h post-final injury, or at 24 h in place of 1H-MRS, in the rostral half of the ipsilateral hippocampus. Lipidomic evaluations were conducted in the ipsilateral hippocampus and cortex.In this experiment, using a well-established awake-closed head injury (ACHI) paradigm to model mTBI, male rats were subjected to a single injury, or five injuries delivered 1 day apart, and injuries were confirmed with a beam-walk task and a video observation protocol. Abundance of several neurometabolites was evaluated 24 h post-final injury in the ipsilateral and contralateral hippocampus using We found that behavioral deficits in the beam task persisted 1- and 4 h after the final injury in rats that received repetitive mTBIs, and this was paralleled by an increase and decrease in hippocampal glutamine and glucose, respectively, whereas a single mTBI had no effect on sensorimotor and metabolic measurements. No group differences were observed in lipid levels and mitochondrial bioenergetics in the hippocampus, although some lipids were altered in the cortex after repeated mTBI.The decrease in performance in sensorimotor tests and the presence of more neurometabolic and lipidomic abnormalities, after repeated but not singular mTBI, indicates that multiple concussions in short succession can have cumulative effects. Further preclinical research efforts are required to understand the underlying mechanisms that drive these alterations to establish biomarkers and inform treatment strategies to improve patient outcomes. Mild traumatic brain injury (mTBI), often referred to as concussion, is a serious medical issue with a 10-fold higher burden on the healthcare system than both moderate and severe traumatic brain injuries (TBI) . mTBIs a1H-MRS. Several neurometabolites, such as N-acetylasparate (NAA), Creatine (Cr), phosphocreatine (PCr), glucose (Glc), glutamate (Glu), and glutamine (Gln), have been identified to have putative utility in assisting mTBI diagnosis and prognosis  were obtained from Monash Animal Research Platform  at postnatal day (PND) 25–27. Three rats were housed per cage with a 12 h:12 h light/dark cycle and food and water available ad libitum. All procedures were approved by the Alfred Medical Research and Educational Precinct Animal Ethics Committee (E/1832/2018/M) and were within the ARRIVE guidelines and the Australian code of practice for the use and care of animals for scientific purposes by the Australian National Health and Medical Research Council. The rats were given 2 days to habituate to their enclosures upon arrival to the facility. Thereafter, they were handled 4 days per week until the first sham or mTBI procedure on PND 38–41.Male Sprague Dawley rats (n = 16/group) were randomly assigned to receive either five sham mTBIs, four sham mTBIs followed by a single mTBI, or five mTBIs, with each procedure occurring 48 h apart , they were immediately exposed to an abbreviated beam task as another confirmation of injury. A full beam task was used to assess sensorimotor function at baseline (1 day prior to the first injury) and 1- and 4 h post-final injury. The rats were trained to traverse the beam 3 days prior to the first sham/ACHI; they were required to cross a 4 cm-wide beam for two consecutive trials at a distance of 10 cm, 25 cm, and 50 cm. This training process was repeated on a 2 cm-wide beam. During the beam test, rats crossed 50 cm of a wooden beam that was 2 cm-wide, and elevated 75 cm above the ground, with a protective mattress placed below to soften any falls. At the start-end of the beam there was a bright light , and at the finish-end there was a darkened bedded “home cage” to encourage task completion. In a small number of cases, trials were re-performed if rats were immobile for longer than 5 seconds. Rats were given a maximal score of 20 s if they had not crossed the beam in this time, or if they fell off the beam, or exhibited two hindlimb slips. Beam slip data from a subset of these rats has been presented previously .in vivo1H-MRS was performed using a 9.4 T Bruker MRI . Rats were anaesthetised with 5% isoflurane inhalation and anesthesia was maintained throughout the 50–60 min procedure with 2% isoflurane inhalation using a nose cone. Body temperature was maintained with a heating pad connected to a water heating circulation system built into the rat cradle. Point Resolved Spectroscopy was acquired in the ipsilateral and contralateral hippocampus with VAPOR water suppression and outer volume saturation. An actively-decoupled, cryogenically-cooled, surface coil was used together with a volume resonator for excitation. Acquisition parameters were: repetition time = 2,500 ms; echo time = 16.5 ms; number of excitations = 256; voxel size = 2.5 × 1.5 × 2.4 mm3; and 4,096 points over 11 ppm. A non-water suppressed spectra was also acquired for absolute quantification of metabolite concentrations relative to water. LCModel was used to process 1H-MRS data with a metabolite basis set matched to field strength and echo time. The current study used absolute concentrations to quantify neurometabolites Glu, Gln, Glc, NAA, Cr + PCr, and GPC + PCh. Only spectra with a %-standard deviation of less than 20 were included in the analyses.Twenty-four hours after the final injury, 1H-MRS; n = 4/group) or 30 h  post-final injury. Thereafter, the rostral half of the ipsilateral hippocampus was dissected and immediately placed in ice-cold buffer A  + 0.4% (w/v) fatty acid free BSA and then homogenized with a glass Dounce homogenizer. The homogenate was centrifuged at 650 g for 5 min at 4°C and the pellet was discarded, and the supernatant transferred to a fresh tube. The supernatant was repeatedly centrifuged at 650 g for 5 min until very little material was pelleted. The supernatant was then transferred to a high-speed centrifuge tube and centrifuged at 10,000 g for 5 min and the supernatant was discarded, and the crude mitochondrial pellet resuspended in isolation buffer A. Mitochondria were re-pelleted by centrifuging at 10,000 g for 5 min, and the supernatant was discarded, and the pellet resuspended in 1 mL of isolation buffer B . The final pellet was collected by centrifuging at 10,000 g for 5 min, discarding the supernatant and resuspending the pellet in isolation buffer B at a ratio of 1.5 mL per 1 g of original weight.To isolate mitochondria, rats were anesthetized via isoflurane inhalation and injected intraperitoneally with Lethobarb (80 mg/kg) and decapitated, either 24-  as previously described . ProteinTo examine sequential electron flow through different complexes of the electron transport chain, an electron flow assay was performed. The electron flow assay was used to determine Complex I, Complex II, and Complex IV mediated respiration with sequential injections of Rotenone 2 μM, Succinate 10 mM, Antimycin-A 4 μM, and L-ascorbate 10 mM + N,N,N′,N′-Tetramethyl-P-Phenylenediamine (TMPD) 100 μM. For the electron flow assay, isolated mitochondria were adhered to 96 well plate in MAS media supplemented with Sodium Pyruvate 10 mM, Malate 10 mM, and FCCP 4 μM.n = 9–12/group). For Lipidomics analysis, tissues were homogenized in PBS and lipids were extracted using a 1-Butanol/Methanol extraction method before application to ESI-MS/MS analysis2, as described previously in detail . To confirm injury after sham/ACHI procedures, video sign observation data was analysed with the non-parametric Friedman’s test. Beam traverse time (and traverse time Ln transformed) and hindlimb slip data was analysed with a one-way analysis of variance (ANOVA) repeated measures  with the Greenhouse–Geisser correction employed. Beam slips were also analysed with Friedman’s test . Linear mixed models were used to account for correlated measurement of hemispheric metabolites within the same animal, with Group, Hemisphere, and Group*Hemisphere entered into the model with subjects entered as a random term. The ROUT method of identifying outliers (Q = 1%) indicated that no outliers in the datasets. ETF and coupling assays were analysed using a two-way ANOVA (with injury and Complex/State as factors). RCR and lipidomic data was analysed using one-way ANOVAs. Multiple comparison Tukey post hoc tests were performed when main effects were statistically significant at p < 0.05. All data was presented as mean ± standard error of the mean (SEM).Statistical analyses were performed with IBM’s SPSS version 27 software, with the exception of linear mixed models, analysed with R software. Before data analyses, Shapiro–Wilk and Levene’s tests were used to evaluate normality and homogeneity of variance, respectively, and non-parametric tests were used for data analyses if either assumption was violated . One-way repeated measures ANOVA revealed a significant between-subject effect of injury  and within-subject effect of time  on the amount of hindlimb beam slips  = 9.75, p < 0.001]. Post hoc analyses revealed that repeated ACHI rats displayed more slips than sham and single ACHI injury rats at 1 h post final injury  and at 4 h post-final injury . Beams slips were also analysed with Friedman tests, with alpha set to 0.017 to correct for multiple comparisons. This confirmed that there were no significant differences observed over time for sham rats (p = 0.758) and rats that received a single injury (p = 0.452); however, a significant difference was found over time within repeat injury rats (p = 0.003), with post hoc testing revealing increased slips when compared to baseline at 1 h (p = 0.005) and 4 h (p = 0.012).Parametric and non-parametric analyses were performed on beam traverse time and hindlimb slip data, which was evaluated as a measure of sensorimotor function at baseline, 1 h, and 4 h post-final injury  = 14.60, p < 0.001] and within-subject effect of time , as well as an interaction effect of injury and time . Post hoc analyses revealed that repeated ACHI rats took significantly longer to traverse the beam at 1 h and 4 h post final ACHI compared to sham  and single ACHI  rats. When the traverse time was Ln transformed to reduce positive skew, we found similar results, with a significant between-subject effect of injury  and interaction effect of injury and time , but no significant effect of time alone . Post hoc testing confirmed that repeatedly injured rats took longer to cross the beam than sham and single injury rats at both 1 h and 4 h (p < 0.001).For average time taken to traverse the beam , one-way1H-MRS was used to measure metabolite concentrations in both hemispheres of the hippocampus at 24 h post-final injury  = 2.39, p = 0.020], but not the single group , compared to sham. Tukey post hoc testing confirmed that repeat ACHI (p = 0.035), but not single ACHI (p = 0.981), increased Gln compared to sham rats (t(56) = −2.23, p = 0.030], but not in the single group ; post hoc analyses revealed a significant decrease in Glc after repeated ACHI (p = 0.029), but not single ACHI (p = 0.337), compared to sham (p ≥ 0.113), NAA (p ≥ 0.467), Cr + PCr (p ≥ 0.388), or GPC + PCh  = 2.24, p = 0.029] and GPC + PCh . When an interaction term was introduced into the model, we found that there was no interaction effect of group*hemisphere on Gln (p ≥ 0.462) or Glc (p = 0.543), but there was an interaction effect of ACHI*hemisphere for NAA  in rats that were injured repeatedly.ham rats . Similar to sham . ACHI ha1H-MRS) or after 1H-MRS at 30 h post-final injury (n = 4–6/group). We did not observe any significant effect of ACHI in the ETF assay (p ≥ 0.307), coupling assay (p ≥ 0.435) or RCR at 24 h post-injury (p = 0.792). However, there was a significant effect of ACHI on RCR at 30 h post-injury (p = 0.037), although no group differences were found with post-hoc testing (sham vs. repeat: p = 0.072). The mitochondrial bioenergetic measures were more variable for rats that were assessed at 30 h post-injury, which may be related to unavoidable variables like combining multiple cohorts of rats for analysis and varied resting times after scans prior to tissue collection.Hippocampal mitochondrial bioenergetics were assessed either 24 h post-final injury  = 3.66, p = 0.038], AC , and BMP , but not AC-OH, CL, LPC, LPC(O), LPC(P), LPE, LPE(P), or LPI (p ≥ 0.113). Rats exposed to repeated ACHIs had higher levels of PE, AC, and BMP compared to rats that had a single ACHI and significantly more than sham rats .239 lipid species were measured over 11 lipid classes, which have previously been associated with brain injury or have been shown to play a role in mitochondrial metabolism, cellular membrane integrity, and cell death. Lipidomic evaluations were carried out first in the hippocampus, but no significant group differences were observed for any lipid of interest (one-way ANOVAs: 1H-MRS at 24 h, we found that concentrations of Gln and Glc were altered by repeated mTBI. Although Gln and Glc are involved in mitochondrial bioenergetics, we did not find evidence of impaired hippocampal mitochondrial respiration in these conditions. Finally, although single and repeated mTBI did alter lipid levels in the hippocampus, more notable repeated mTBI effects were found in the cortex.The main findings of this report provide evidence that repeated mTBIs produce worse neurobehavioral outcomes than a single mTBI, and that these outcomes are accompanied by alterations in multiple neurometabolites and lipids. First, we confirmed concussive-like injuries for all ACHIs with a clinically relevant video sign observation protocol; however, only rats given repeated mTBIs had deficits in the sensorimotor beam-walk task at 1- and 4 h post-final injury. Secondly, with In the present study, we used the well-characterized ACHI mTBI model, which consistently produces neurobehavioral impairments in the absence of skull fracture, significant focal contusion, or other complications that can occur in other animal models of mTBI . Video o1H-MRS has been postulated to have clinical utility as a diagnostic and prognostic biomarker for individuals with suspected concussion (1H-MRS. Here we evaluated the effect of single and repeated ACHIs on the concentration of several neurometabolites that have been implicated in mTBI, namely Glu, Gln, Glc, NAA, Cr + PCr, and GPC + PCh (Mounting evidence suggests that alterations in neurometabolite concentrations, due to factors induced by trauma, such as disruption of neuronal membranes, axonal stretching, and ion channel dysregulation, are implicated in the neurobehavioral sequalae of mTBI . As suchncussion . Corroboncussion  and repencussion  brain inPC + PCh .1H-MRS, but we did observe a marked increase of Gln in the hippocampus 24 h after repeated but not singular mTBI. This may be due to the astrocytic uptake of excess Glu that is released acutely in response to TBI (1H-MRS following repeated mTBI in rodents. This decrease may be explained by a reduction in Glc uptake, as observed in several animal and human TBI studies (Due to the similarity of the molecular structures of Glu and Gln, resulting in similar peaks on the spectra, early clinical studies traditionally combined the absolute concentrations of Glu and Gln . This mee to TBI  and subse to TBI . Future e to TBI . This st studies , or perh studies . However1H-MRS is poorly understood; further research efforts are required if we are to better understand these mechanisms, which could lead to the discovery of therapeutic targets and biomarkers of brain injury.We did not detect alterations in the hippocampal concentrations of NAA, Cr + PCr, or GPC + PCh between injury groups. These findings are consistent with some clinical  and rodeAlthough mTBI is thought to lead to a period of increased cerebral vulnerability that may be contributed by a dysregulation of bioenergetics , the assRegion-specific lipid alterations induced by mTBI likely depend on the nature of the injury and its pathophysiological consequences. We found that mTBI-induced lipid alterations were more pronounced in the cortex – where repeated ACHIs elevated PE, AC, and BMP – than in the hippocampus, where no significant group differences were observed. These findings are consistent with a closed-head mTBI study that reported elevations in cortical PE levels 24 h after repeated mTBI in Tau transgenic mice, but not in the hippocampus. Notably, this study also found that PE levels had normalized 3- and 12 months post-injury . HoweverThere are several limitations in this study. First, only male rats were included, even though evidence suggests that there are sex-based differences in various aspects of the response to mTBI , 2022, i1H-MRS, but these alterations were not associated with hippocampal mitochondrial dysfunction. Lipids PE, BMP, and AC were elevated in the cortex after repeated mTBI, but not the hippocampus. Our data supports the fact that repeated mTBIs in short succession can have cumulative effects, evidenced by the poorer performance in sensorimotor tests when rats received repeated vs. singular injuries, as well as greater neurometabolic and lipidomic abnormalities. More research is needed to understand the underpinning neuropathological changes responsible for these alterations.Alterations in metabolites Gln and Glc were observed after repeated mTBI in the hippocampus using The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by Alfred Research Alliance Animal Ethics Committee.LP, SB, SS, BD, and SM contributed to the study design. LP, SB, WO’B, SS, BD, DW, and SM contributed to data collection. JA completed data analysis and drafted the manuscript. GS assisted data analysis. All authors contributed to the article and approved the submitted version.This work was supported by the National Health and Medical Research Council to DW [grant number: 1174040] and SS [grant number: 1141643].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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We found that ψ decreased from 0.369 ± 0.088 in 1997 to 0.248 ± 0.054 in 2013; γ was lower during the dry season (May–September) than the wet season (October–April) and was positively influenced by total seasonal rainfall . Mean mammalian species richness decreased from 11.943 ± 0.461 in 1997 to 7.185 ± 0.425 in 2013. With highly variable climatic patterns expected in the future, especially increased frequency and intensity of droughts, it is important to monitor small mammal communities inhabiting threatened California oak woodlands.The richness and composition of a small mammal community inhabiting semiarid California oak woodland may be changing in response to climate change, but we know little about the causes or consequence of these changes. We applied a capture‐mark‐recapture model to 17 years (1997–2013) of live trapping data to estimate species‐specific abundances. The big‐eared woodrat was the most frequently captured species in the area, contributing 58% of total captures. All small mammal populations exhibited seasonal fluctuations, whereas those of the California mouse, brush mouse, and pinyon mouse declined during the study period. We also applied a multispecies dynamic occupancy model to our small mammal detection history data to estimate species richness, occupancy ( We used dynamic occupancy models to understand the temporal variation of community parameters such as species richness, extinction, and colonization dynamics. However, between the 1970s and 2000s, the relative abundance of red‐backed voles increased by 22%, with a corresponding decrease in the relative abundance of deer mouse by 22%. More recently, red‐backed voles have been the most dominant species, representing 63% of all small mammal captures. Thus, a major shift in the relative abundances of constituent species occurred without a detectable change in species richness , local extinction (ϵ), and colonization (γ) probabilities.Although the population ecology of some small mammals inhabiting the coastal California oak woodlands is well understood  than the dry season (May–September); and (ii) ϵ would be negatively affected, while γ and species richness would be positively affected by current and past seasonal rainfall and by the El Niño index . With the predictions that severe droughts are likely to be more frequent and more intense in coastal California  with scattered buck brush (Ceonothus cuneatus) shrubs. The ground layer was dominated by Mediterranean annual grasses, predominantly Avena and Bromus spp., with scattered native bunch grasses (Nassella and Festuca spp.). In the more mesic areas, mixed stands of blue oak and coast live oak (Quercus agrifolia) occurred with a diverse shrub understory of toyon (Heteromeles arbutifolia), California coffee berry , spiny red berry (Rhamnus crocea), buck brush (Ceanothus cuneatus), and bigberry manzanita (Arctostaphylos glauca). Grass cover was sparser than in the blue oak stands and intermixed with a diverse assemblage of native and exotic forbs, including wild peony , hummingbird sage , deerweed (Lotus scoparius), and miner's lettuce (Claytonia perfoliata). Patches of poison oak (Toxicodendron diversilobum) occurred throughout the study area. Within these vegetation communities, the main suspected predators of small mammals were several species of owls: the barn owl , great horned owl (Bubo virginianus), and California spotted owl . Snake species included the northern Pacific rattlesnake , Pacific gopher snake (Pituophis catenifer catenifer), and California kingsnake . Large mammalian predators that frequented the study area were the mountain lion (Puma concolor), bobcat (Lynx rufus), coyote (Canis latrans), and gray fox (Urocyon cinereoargenteus).Our study was carried out from 1997 to 2013 at the Army National Guard Post, Camp Roberts, in coastal‐central California, USA , black‐tailed deer (Odocoileus hemionus), feral pigs (Sus scrofa), and wild turkeys  during abbreviated seasons within the statewide hunting seasons.The last wildfire within the study occurred in 1953. During the study, there was no disturbance by military activities. Public access to Camp Roberts was restricted to hunting for California quail  using Sherman live traps  for three trap nights on grids with 15‐m spacing (64 traps per grid). During spring 1997 to spring 2013, we trapped on 22 grids (33 sampling occasions) for 139,392 trap nights . In fall 2013, 21 of the 22 grids were trapped (4032 trap nights). During the 17 years of study, we trapped during 34 sampling occasions and for 143,424 trap nights. At each capture, we recorded the capture location, and the species and sex of each animal. At first capture, we tagged animals in the right ear with a Monel 1.00S animal tag . We released all animals at the location of capture. Trapping and handling of animals followed the guidelines from the University of California, Berkeley, Institutional Animal Care and Use Committee , and the guidelines of the American Society of Mammalogists  were captured at least once, but the captures were not recorded. For these two species, we randomly assigned primary and secondary sampling occasions for when they might have been captured.In traditional dynamic occupancy (DO) analyses, each site  appears as a row and each sampling occasion appears as a column in the detection‐history matrix. For our application, however, we are interested in estimating the probability that each small mammal species from our pool of regional species is present on our study area. Hence, each species appeared as a row, and each sampling occasion as a column in the detection‐history matrix. During our study, 10 species of small mammals were captured and entered into our database. An additional two species , we determined that 18 additional species of small mammals could have been present on our study area but were not detected during our study  are captured or detected during surveys of natural animal populations  and negatively influence extinction (ϵ). Regional climatic phenomena such as El Niño can potentially influence the DO parameters, so we also tested for the influence of the Oceanic Niño Index (ONI) on γ and ϵ. We obtained rainfall data for the years 1997 to 2013 from National Oceanic and Atmospheric Administration (https://www.ncei.noaa.gov/cdo‐web/). We also allowed γ and ϵ to vary between the dry (May–September) and wet (October–April) seasons, with the expectation that colonization would be higher and extinction probability lower during the wet season compared to the dry season. Furthermore, capture probability (p) and occupancy (ψ) can differ between commonly detected species and those that were rarely or never detected; thus, we also included species‐detection status as covariates for p and ψ. Species that were captured ≥1000 times during this study were considered commonly detected species (coded “1”), whereas those that were captured <1000 times or never captured during the study period were considered rarely detected species (coded “0”).Current or past rainfall is known to influence survival and recruitment of small mammals in our study area , occupancy (ψ), and probabilities of time or season‐specific local extinction (ϵt), and colonization (γt). The occupancy probabilities for second and subsequent primary sampling occasions are then calculated recursively as represented in MacKenzie et al.  is determined by the species pool in the larger region , and the El Niño index. Finally, we tested for the influence of seasonal rainfall (current and past) and El Niño additively and interactively with season (dry or wet season).We implemented the DO model using program PRESENCE via the RPresence package Hines,  for progWe used an information‐theoretic approach using the Akaike Information Criterion (AIC) values for model selection and statistical inference , we used the superpopulation (or POPAN) capture‐mark‐recapture (CMR) modeling approach , California ground squirrel, Botta's pocket gopher, and Western harvest mouse  were rarely captured. Marginally more captures occurred during spring  than fall . Five species , and California pocket mouse) were captured in every season, while three species were captured only in seven seasons or fewer .Our sampling effort of 143,424 trap nights resulted in 45,174 captures of 12 small mammal species. The big‐eared woodrat was the most commonly captured species  followed by pinyon mouse (7938 captures), brush mouse (4326 captures), California pocket mouse (3649 captures), and California mouse (2176 captures). The big‐eared woodrat represented 22%  to 82% (2010 spring) of total captures. The pinyon mouse and brush mouse were the two other frequently captured species, representing 9.65% (2010 spring) to 36.89% , and 3.17%  to 33.33% , respectively, of total captures  and total seasonal rainfall lagged two seasons , but negatively correlated with total seasonal rainfall lagged one season  = 0.852 ± 0.015, local colonization γ=0.029±0.007, and extinction probability ϵ=0.088±0.020. However, the most parsimonious model , γ was lower during the dry than the wet season ; and ϵ was low and quite stable during the study period (0.037 ± 0.012). However, this model could not estimate standard error for ψ for the frequently detected species group; consequently, this model could not be used to estimate time‐specific ψ. Thus, we used another model that received similar support . Seasonally, ϵ was higher during the dry season than during the wet season . Small mammal occupancy decreased over time from 0.369 ± 0.088 in 1997 to 0.248 ± 0.054 in 2013  of the top model containing a particular covariate. We found evidence for a positive influence of total seasonal rainfall on γ ; 95% CI for β parameters relating climatic covariates to γ and ϵ straddled zero.Inclusion of climatic covariates considered in this study did not substantially improve model parsimony Table . Nonethe4Across continents, community dynamics of small mammals in semiarid environments is strongly influenced by rainfall patterns . The abundance of pinyon mouse showed strong positive correlation with the abundances of brush mouse  and California mouse , which might warrant further attention. Also, despite the positive association of rainfall with the γ of small mammals, except for the California pocket mouse, the abundance of individual species did not significantly correlate with rainfall.During this study period, 12 species of small mammals were captured at least once, but the number of species captured varied from a minimum of six species in the fall of 2008 and 2013 to a maximum of 11 species in the spring of 1997. The big‐eared woodrat was overwhelmingly the most dominant species throughout the study in terms of abundance. Big‐eared woodrats achieved relatively high densities in 1997, 2007, and 2011; they occurred in low numbers during 2000–2004. On average, the abundance of the California mouse, brush mouse, and pinyon mouse declined during this study, whereas all of the small mammal populations showed seasonal and/or random annual fluctuations. Seasonal fluctuations in California pocket mouse abundance was because they remain underground and unavailable for capture during the spring season. Heermann's kangaroo rat and California ground squirrel were infrequently captured before 2001 and were not captured after that year. We are not sure whether they went locally extinct or simply did not get captured in later years. We postulated that the abundance and potential dominance of the big‐eared woodrat would negatively influence the abundance of other species  was higher than colonization probability (γ) at Camp Roberts. Being a semiarid area, we expected γ to be higher and ϵ to be lower during the wet season compared to dry season at Camp Roberts. Consistent with our hypothesis, estimates of γ were substantially higher during the wet season, with near zero γ during the dry season. This higher γ during winter is possibly facilitated by the positive influence of rainfall on the survival and recruitment of small mammals, which is well documented in arid areas  and the dusky‐footed woodrat (Neotoma fuscipes) in northern California, but found no clear evidence that these extinction events were triggered by climatic variables. However, evidence suggests that local extinctions do occur and that species richness might decrease under the scenario of changing climate ; formal analysis ; writing – original draft ; writing – review and editing . William D. Tietje: Conceptualization ; funding acquisition (lead); project administration (lead); validation ; writing – review and editing . Anne Y. Polyakov: Formal analysis ; writing – review and editing . James E. Hines: Methodology ; validation ; writing – review and editing . Madan K. Oli: Conceptualization (lead); data curation ; investigation ; methodology ; supervision (lead); validation ; writing – review and editing .We have no competing interests.https://datadryad.org/stash/share/X_YsIVeKdAKEGsChqyKJaY4JzYQv‐Y1ILSbSF9jzg9M].This article has earned a Preregistered Research Designs badge for having a preregistered research design, available at [Appendix S1.Appendix S2.Click here for additional data file.


P < .001) and NLR on the 7th postoperative day  with prognosis of aSAH patients. The results of receiver operating characteristic curve showed that cutoff values of RDW and NLR for predicting the prognosis in aSAH patients were 13.05% and 6.97, respectively. Higher RDW on the 3rd postoperative day and NLR on the 7th postoperative day were possibly associated with poor prognosis of aSAH patients. We should pay attention to the RDW and NLR levels during different hospitalization periods, especially in the short postoperative period. Moreover, the cutoff values for predicting prognosis need to be validated in larger-sample studies.The associations of neutrophil to lymphocyte ratio (NLR) and red cell distribution width (RDW) with the prognosis of aneurysmal subarachnoid hemorrhage (aSAH) patients were confirmed in a few studies. But NLR and RDW levels in most of these studies were on admission. Here we carried 1 retrospective study including 150 patients with aSAH who underwent surgeries in the First Affiliated Hospital of Yangtze University from January 2020 to February 2023 to explore the associations on the preoperative day, the first (1st), third (3rd), and seventh (7th) postoperative days. The level of RDW on the 3rd postoperative day and level of NLR on the 7th postoperative day in patients with poor prognosis were significantly higher than patients with good prognosis. The results of multivariate logistic analysis also confirmed the associations of RDW on the 3rd postoperative day (OR = 16.785, 95% CI: 4.077–69.107,  Previous studies confirmed that NLR was associated with the occurrence, severity, and prognosis of some diseases. NLR was associated with the functional outcomes in patients with cerebral hemorrhage and acute cerebral infarction.,5 On the other hand, thrombosis was also involved in the pathological changes after aSAH. Red cell distribution width (RDW) was a indicator reflecting the heterogeneity of red blood cell size and confirmed to be important and possibly associated with prognosis of patients in many diseases such as ischemic stroke, pulmonary embolism, and tumors.Aneurysmal subarachnoid hemorrhage (aSAH) is a complex and fatal disease. Although breakthroughs have been made in the treatment of aSAH in recent years, the prognosis of some patients is still poor. The important roles of some inflammatory indicators were demonstrated in the progression of cerebrovascular diseases and possibly related to the prognosis in some studies.,11 However, the NLR and RDW levels in most of these studies were on admission. The disease condition was variable during hospitalization. Therefore, we explored the associations on the preoperative day, 1st, 3rd and 7th postoperative days in this study.There were a few studies that explored the associations of NLR and RDW with the outcome of aSAH patients, and suggested that higher NLR and RDW were associated with poor prognosis.We carried one retrospective study including 150 patients with aneurysmal subarachnoid hemorrhage who underwent surgeries in the First Affiliated Hospital of Yangtze University from January 2020 to February 2023. The study was approved by the Ethics Committee of the First Affiliated Hospital of Yangtze University. Patients meeting the following criteria were included: (1) had subarachnoid hemorrhage confirmed by computed tomography (CT); (2) had intracranial aneurysm confirmed by digital subtraction angiography (DSA) and associated with bleeding; (3) received craniotomy clipping or intravascular embolization surgeries after admission. Patients meeting the following criteria were excluded: (1) had subarachnoid hemorrhage caused by arteriovenous malformations and trauma (or did not have aneurysms); (2) suffered from severe systemic diseases, such as severe liver dysfunction, kidney dysfunction, heart dysfunction, hematological diseases, etc.; (3) had Modified Rankin Scale (mRS) score ≥ 3 before admission; (5) were unable to complete follow-up of 3 months. The flow chart was showed in Figure rd, and 7th postoperative days. NLR was the ratio of neutrophil to lymphocyte. The prognosis was determined by the Modified Rankin Scale (mRS) results of 90-d follow-up after morbidity. The patients with scores of mRS 0–2 were defined as having a good prognosis, while mRS 3–6 were defined as having a poor prognosis.Baseline characteristics included gender, age, Hunt-Hess grade, Glasgow Coma Scale (GCS) score, combination of intraventricular hemorrhage, the location of aneurysm, neck length of aneurysm, date of surgery, time length of surgery. The blood cells counts were extracted from the hospital information system including white blood cells, neutrophils, lymphocytes, hemoglobin, and the red blood cell distribution width on the preoperative day, the 1st, 3t test was used for analysis. If the quantitative variables met the normal distribution but variances were not homogeneous, corrected t test was used. If the normal distribution could not be satisfied, Mann–Whitney U test was used for analysis. The qualitative variables were compared by Pearson chi-square test, Continuity correction or Fisher exact test. The multivariate logistic regression model was used for factors associated with prognosis. Covariates that had a P-value >.05 in the univariate analysis were added to the multivariate logistic analysis. ROC (receiver operating characteristic) curve was used for the cutoff values of characteristics for predicting poor prognosis in patients with aSAH. Graphpad prism 6.0  was used for the correlation analysis. All p-values were 2-sided and the statistical significance was set at P < .05.We used SPSS 23.0  for data analysis. If the quantitative variables met the normal distribution and variances were homogeneous, independent-sample P < .05 , P = .001. The preoperative counts of neutrophils in patients with good prognosis and poor prognosis were (9.96 ± 4.00) × 109/L and (12.77 ± 5.16) × 109/L respectively, with significant difference, P = .001. The preoperative counts of lymphocytes had no significant difference, P = .379. The preoperative NLR in patients with good prognosis and poor prognosis were 10.69 ± 9.41 and 11.50 ± 10.25 respectively, with no significant difference (P = .461). The median preoperative RDW in patients with good prognosis was 12.7%, significantly lower than patients with poor prognosis (12.9%), P = .017. There was no significant difference in preoperative hemoglobin between patients with good prognosis and poor prognosis, P = .356  × 109/L, significantly lower than patients with poor prognosis ([12.57 ± 5.70] × 109/L), P = .001. The counts of neutrophils in patients with good prognosis and poor prognosis were (8.72 ± 3.63) × 109/L and (11.14 ± 5.58) × 109/L respectively, also with significant difference, P = .001. The counts of lymphocytes in patients with good prognosis and poor prognosis were (0.95 ± 0.39) × 109/L and (0.83 ± 0.37) × 109/L, with no significant difference, P = .122. The NLR ratio on the 1st postoperative day in patients with good prognosis was 9.86 ± 6.32, significantly lower than patients with poor prognosis (14.37 ± 13.05), P = .001. The median RDW level in patients with good prognosis was 12.90%, significantly lower than that patients with poor prognosis (13.25%), P = .001. The levels of hemoglobin (P = .553) on the 1st postoperative day in patients with good prognosis and poor prognosis had no significant difference  × 109/L, significantly lower than patients with poor prognosis ([11.54 ± 4.40] × 109/L), P < .001. The counts of neutrophils in patients with good prognosis and poor prognosis were (7.47 ± 2.79) × 109/L and (9.78 ± 3.11) × 109/L respectively, with significant difference, P < .001. The counts of lymphocytes on the 3rd postoperative day were (1.05 ± 0.68) × 109/L and (0.79 ± 0.64) × 109/L respectively, with significant difference, P = .013. The median NLR level in patients with good prognosis was 6.46, significantly lower than patients with poor prognosis (10.30), P < .001. The median RDW in patients with good prognosis on the 3rd postoperative day was 12.40%, significantly lower than patients with poor prognosis (13.25%), P < .001. There was no significant difference in hemoglobin level between patients with good prognosis and poor prognosis, P = .063  × 109/L, significantly lower than patients with poor prognosis ([12.54 ± 4.16] × 109/L), P < .001. The counts of neutrophils in patients with good prognosis and poor prognosis were (7.21 ± 2.67) × 109/L and (10.27 ± 3.95) × 109/L respectively, with significant difference, P < .001. The counts of lymphocytes on the 7th postoperative day were (1.26 ± 0.62) × 109/L and (1.09 ± 0.59) × 109/L respectively, with significant difference, P = .033. The median NLR level in patients with good prognosis was 5.63, significantly lower than patients with poor prognosis (9.12), P < .001. The median RDW in patients with good prognosis on the 7th postoperative day was 12.40%, significantly lower than patients with poor prognosis (13.20%), P < .001. There was no significant difference in hemoglobin level between patients with good prognosis and poor prognosis, P = .367  × 10P = .008). On the 1st postoperative day, the proportions of cases with lower quartiles of RDW and NLR were significantly lower than cases with higher quartiles (P = .004). On the 3rd postoperative day, the proportions of cases with lower quartiles of RDW (P = .001) and NLR (P < .001) were significantly lower than cases with higher quartiles. On the 7th postoperative day, the proportions of cases with lower quartiles of RDW (P = .003) and NLR (P < .001) were significantly lower than cases with higher quartiles.As showed in Table P < .001), RDW  on the 3rd postoperative day and NLR  on the 7th postoperative day were associated with the prognosis of aSAH patients , and the Youden index was 0.428. The sensitivity and specificity of RDW ≥ 13.05% on the 3rd postoperative day for predicting poor prognosis were 67.6% and 75.2% respectively , and the Youden index was 0.427. The sensitivity and specificity of NLR ≥ 6.97 on the 7th postoperative day for predicting poor prognosis were 41.2% and 89.9%   cases with hematoma dilation. The higher white blood cells at admission, the lower risk of hematoma dilation. Hematoma dilation was negatively correlated with the count of neutrophils.White blood cells and neutrophils were once considered sensitive biomarkers and proven to be associated with the prognosis of stroke. Tao C et al conducted a retrospective study exploring the associations between counts of blood cells and scores of mRS at 90 days in 336 patients with spontaneous cerebral hemorrhage. The results of multivariate analysis showed that higher white blood cells and neutrophils were associated with poor prognosis. Moreover, linear regression analysis showed that counts of these blood cells were significantly correlated with Glasgow Coma Scale score and cerebral hemorrhage volume at admission. In another study including 192 patients, there were 54 (28.1%) cases had neurological deterioration. NLR was independently correlated with neurological deterioration. NLR was the best variable for predicting poor outcomes. In our study, the associations of white blood cells, neutrophils, lymphocytes and NLR on different days with the 3-month prognosis were explored. The results of univariate analysis showed that counts of white blood cells and neutrophils on the preoperative, the 1st, the 3rd and the 7th postoperative days were all associated with prognosis. The results also showed that counts of lymphocytes on the 3rd and the 7th postoperative days, levels of NLR on the 1st, the 3rd and the 7th postoperative days were associated with prognosis. However, the result of multivariate logistic analysis only confirmed the association between NLR on the 7th postoperative day and the prognosis of aSAH patients.NLR was the ratio of neutrophil to lymphocyte, combining the changes of the 2 blood cells, with more efficacy and accuracy compared to a single indicator for predicting inflammatory response. Sun Y. et al conducted one prospective study in 352 patients with acute cerebral hemorrhage. All patients were divided into different groups based on the NLR levels at admission to evaluate the associations of NLR with hematoma volume, disease severity, and prognosis. After a 3-month follow-up, patients with NLR ≥ 7.85 had higher NIHSS scores and more hematoma volumes, but not poor 3-month prognosis. Lymphocytes and neutrophils were found in the brain in areas of acute ischemic stroke. Post-stroke immunosuppression was also observed after stroke. NLR ≥ 5.9 was associated with pulmonary edema and high fever. Among patients with aSAH, whether a higher NLR level represents a severe or acute stress, or increased neutrophils and decreased lymphocytes originate from immunologic response to acute aSAH remains unknown. Delayed neuroinflammation or systemic inflammatory response syndrome after subarachnoid hemorrhage possibly resulted in a high NLR, which could be the trigger of secondary harmful hematologic or immunologic host responses. Neuroinflammation after acute cerebral hemorrhage could be reflected by acute leukocytosis and decline of lymphocytes. Infiltrated neutrophils could release inflammatory cytokines, and enhanced perifocal edema by promoting capillary permeability, cell swelling, and blood-brain barrier damage. Lymphocytes were the main immune regulatory cells, and their decrease and inactivation were markers of brain injury, which were the results of excessive activity of the sympathetic nervous system. On the other hand, patients with lower lymphocytes and higher neutrophils possibly developed concomitant infections, which also increase the risk of poor prognosis. Unfortunately, we did not pay attention to the characteristics of infected patients.NLR can get affected in systemic inflammatory response syndrome, sepsis, post-surgical state and many other confounders. The patients with abdominal infection had elevated counts of neutrophils before surgery and low lymphocyte counts. Critically ill patients with severe sepsis or septic shock had significantly highest values of neutrophil relative counts and marked lowest values of lymphocyte counts. One systematic review and meta-analysis exploring the association of RDW with prognosis in aneurysmal subarachnoid hemorrhage patients confirmed that higher RDW was associated with a worse functional outcome, a worse discharge and 3-month functional outcome, and a higher in-hospital and 90-day mortality. In another study carried in 8175 community-dwelling adults 45 years or older and exploring the association between RDW and risk of death, the results showed that RDW was a widely available test and a strong predictor of mortality in the general population of adults 45 years or older. In our study, the univariate analysis results showed that the levels of RDW on the preoperative day, the 1st, 3rd and 7th postoperative days were all associated with the prognosis of aSAH patients, further suggesting that RDW might be a highly advantageous prognostic indicator. Moreover, the results of multivariate analysis confirmed the association between level of RDW on the 3rd postoperative day and the prognosis. The results by ROC curve in our study also showed that the cutoff value of RDW for predicting the prognosis in aSAH patients was 13.05%. The sensitivity and specificity of RDW ≥ 13.05% for predicting poor prognosis were 67.6% and 75.2%, both relatively high. Some possible mechanisms by which higher RDW was associated with poor prognosis in aSAH patients were explored. Higher RDW levels were associated with endothelial dysfunction and increased levels of inflammatory cytokines, and reflected the potential pro-inflammatory state.,25 The higher the RDW level, the greater the likelihood of thrombosis formation in patients. The increase of RDW level indicated the increase of immature reticulocytes, which had worse deformability and stronger thrombolytic ability than mature erythrocytes. Deformed erythrocytes could accelerate the process of platelet aggregation, damage blood vessels, and reduce blood flow. Higher RDW levels were also associated with lower antioxidant levels and higher oxidative stress levels, which could weaken the deformability of erythrocytes, increase adhesion between erythrocytes and endothelial cells, and promote thrombosis. In addition, the lifespan of erythrocytes was approximately 120 days. Therefore, RDW possibly reflect the long-term inflammatory state of aSAH patients.There were a few studies on the association of RDW with prognosis of aSAH patients. The results of one study including 274 patients with aSAH admitted to the intensive care unit confirmed a significant association between RDW levels and mortality.The prognosis differences among patients with different quartiles of RDW and NLR in Table Our study have some limitations. First, the dataset had some bias. There were two-thirds or over cases with a good Hunt-Hess grade (I/II) in all patients or in patients with good outcome, one-third or less cases with a poor Hunt-Hess grade (III/IV/V). The good outcome was possibly driven in general by a favorable natural history due to minor SAH in the majority of patients with a good Hunt-Hess grade. Second, the patients with good outcome by far outweighed the patients with poor outcome, so the patients with poor outcome might be underpowered. Third, the number of patients was not large enough, which possibly caused some bias. We will investigate the associations in larger-sample study. Finally, characteriazatioin of patients who developed hospital or out-hospital infections did not get noticed in our manuscript, which was an additional limitation.In summary, higher RDW on the 3rd postoperative day and NLR on the 7th postoperative day were possibly associated with poor prognosis of aSAH patients. We should pay attention to the RDW and NLR levels during different hospitalization periods, especially in the short postoperative period. Moreover, the cutoff values for predicting prognosis need to be validated in larger-sample studies.We are thankful to all the medical staff in the Neurointensive care unit and the statistician, in The First Affiliated Hospital of Yangtze University.Conceptualization: Jie Min, Yongfeng Zhao, Xian Wang.Data curation: Xian Wang.Formal analysis: Yongfeng Zhao.Methodology: Jie Min, Jian Zhao.Project administration: Xian Wang, Jian Zhao.Writing – original draft: Jie Min, Yongfeng Zhao.Writing – review & editing: Jie Min, Xian Wang.


GSE3218, 24 differentially expressed immune genes (immDEGs) were identified. A new risk signature consisting of six immDEGs was developed using these genes. Individuals in the high‐risk group had poor overall survival . We validated the six‐immDEGs risk signature in pure seminoma and mixed TGCT types. Two distinct immune patterns (Cluster 1 and Cluster 2) were identified using the consensusclusterplus, and Cluster 1 possessed an unfavorable OS compared with Cluster 2 . Cluster 1 patients had significantly lower naive B cells, memory B cells, plasma cells, naive CD4 T cells, gamma delta T cells, and activated dendritic cells than Cluster 2 patients. Genes relating to the WNT signaling pathway, TGF‐β signaling pathway, antigen processing and presentation, and NK cell‐mediated cytotoxicity were associated with TGCT. STC1 was elevated in TGCT tissues, and its high expression showed advanced clinicopathological characteristics and poor prognosis of TGCT. Our findings may contribute to an increased understanding of the onset and progression of TGCT.Testicular germ cell tumors (TGCT) are the most common type of testicular cancer, comprising 90–95% of cases and representing the most prevalent solid malignancy in young adult men. Immune infiltrates play important regulatory roles in tumors, but their role in TGCT remains unclear. Molecular subtyping is a promising way to provide precisely personalized treatment and avoid unnecessary toxicities. This study investigated immune infiltrates, key biomarkers, and immune subtyping of TGCT. In  In this study, a risk signature comprising immune‐related genes was successfully developed and shown to be effective in predicting the prognosis of patients with testicular germ cell tumors (TGCT). We also identified testicular cancer immune infiltrates and novel immune cell subtypes. These findings may aid in comprehending the onset and progression of TGCT and devising efficient treatment approaches. BPsbiological processesCCscellular componentsDEGsdifferentially expressed genesFCfold changeGEOgene expression omnibusGOgene ontologyGSEAgene set enrichment analysisH&Ehematoxylin and eosinIHCimmunohistochemicalimmDEGsdifferentially expressed immune genesIRSimmunoreactivity scoreKEGGKyoto Encyclopedia of Genes and GenomeKMKaplan–MeierLASSOleast absolute shrinkage and selection operatorMFsmolecular functionsOSoverall survivalPD‐L1programmed death receptor ligand‐1PPIprotein–protein interactionROCreceiver operating characteristicSTC1stanniocalcin‐1STRINGSearch Tool for the Retrieval of Interacting GenesTGCTtesticular germ cell tumorsTMEtumor microenvironmentUMAPuniform manifold approximation and projectionTesticular cancer is the most prevalent solid malignancy among young adult men , 2. The et al. [et al. [The immune cells in the tumor microenvironment (TME) are critically involved in tumorigenesis , 7, and et al.  found th [et al.  highligh [et al. , 12, 13.The present research employed gene expression profiles to assess immune‐related genes that exhibited a significant difference between individuals with TGCT and control samples. A risk signature comprising immune‐related genes was successfully developed and is effective in predicting patient prognosis. Moreover, two distinct immune patterns with significant prognostic differences were identified. The identification of variations in immune infiltration patterns between TGCT and healthy tissues would aid in comprehending the onset and progression of TGCT and devising efficient treatment approaches.GSE3218 (GPL96) and GSE3218 (GPL97) were provided by the Gene Expression Omnibus (GEO) database [https://www.aclbi.com/). The MSigDB databases were used to obtain 1811 immune‐related genes [The datasets database . To genedatabase . The mered genes .n = 8) were obtained from patients with prostate cancer undergoing orchiectomy from January 2018 to January 2019. Two pathologists confirmed all tissue types using hematoxylin and eosin (H&E) staining. The study was conducted in accordance with the Declaration of Helsinki. All individuals granted their written informed consent prior to participation. The approval of this study was granted by the Ethics Committee of the Affiliated Hospital of Jining Medical University (number: 2023‐04‐C038).A total of 24 TGCT (seminoma) tissues were retrieved from January 2016 to September 2022 in Affiliated Hospital of Jining Medical University. And normal testicular tissues  between tumors and control samples were identified utilizing the ‘ package  . The ‘glmnet’  and leasr package ‘consensusclusterplus’ [r package ‘limma’ was utilized to extract DEGs between two distinct immune patterns | > 1.5). The ROC curve was used to evaluate the ability of genes to distinguish immune subgroups.The terplus’  was emplcytoscape software (https://cytoscape.org/).The Search Tool for the Retrieval of Interacting Genes  (STRING)r packages ‘org.hs.eg.db’ and ‘clusterprofiler’ [P‐value of < 0.05 was set as the criterion. To explore the variations in BPs between distinct subgroups, gene set enrichment analysis (GSEA) [P‐value of < 0.05 was taken to be a statistically significant criterion for this analysis.For gene ontology (GO) annotation analysis in terms of molecular functions (MFs), biological processes (BPs), and cellular components (CCs), as well as Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, the rofiler’  were emps (GSEA)  was carrP‐value of < 0.05 was used to determine the significance of the results.The analytical tool CIBERSORTx  can use The TGCT and normal testicular tissues were subjected to an immunohistochemical (IHC) assay using a 1 : 100 dilution of an antibody against STC1 . Paraffin wax was used for embedding the tissues. IHC was employed according to a standard method. A final immunoreactivity score (IRS) was obtained for each case by multiplying the positive cell percentage score  and the intensity score . Divided into four levels: 0–2: negative (−), 3–5: weak positive (+), 6–8: positive (++), 9–12: strong positive (+++).rstudio 4.1.3 . The independent Student's t‐test  and Mann–Whitney U‐test  were conducted to compare two groups of continuous variables. Pearson correlation analysis was conducted to estimate correlation coefficients between distinct variables. A P‐value of < 0.05 suggested the significance level. Other statistical methods have been mentioned in the corresponding ‘All the statistical analyses were carried out utilizing ESRP1 (FC = 5.42), SOCS3 (FC = 4.92), PGK1 (FC = 4.77), CTSB (FC = 4.72), and GBP1 (FC = 4.71). The five most significantly downregulated genes were CABYR (FC = 0.06), KHDRBS3 (FC = 0.08), PRND (FC = 0.10), TSGA10 (FC = 0.12), and GSTM3 (FC = 0.13). By intersecting DEGs with immune‐related genes, a total of 24 immDEGs were identified. Among these, 15 were upregulated, while nine were downregulated  were identified utilizing the us’ Fig. . Clusterus’ Fig. . Figure r 1 Fig. . Moreovecytoscape software, according to the data from the STRING database, to explore their relationships. The PPI network of DEGs demonstrated that the distribution of the 24 immDEGs was relatively scattered  was constructed using red Fig. . In the EGs Fig. . FollowiEGs Fig. . SubsequEGs Fig. .A PPI network was constructed for the DEGs between the two immune patterns Fig. . SimilarImmune cell infiltration was estimated using CIBERSORTx. Figure The correlation between the immune cell contents of samples in all individuals of Cluster 1 and Cluster 2 was calculated. In all individuals, a significant positive correlation was observed between naïve B cells and native CD4+ T cells, whereas the remaining cell types primarily showed negative correlations Fig. . In ClusFinally, the correlations between 24 immDEGs and immune cell types were calculated in all patients Fig. , individP = 0.02; Fig. P = 0.03; Fig. TGCT is the most common type of testicular cancer. Seminoma is the most common histologic subtype of TGCT in young men. Mixed type of TGCT is the most common type of nonseminoma. Pure embryonal carcinoma, teratoma, and yolk sac tumor are rare. Therefore, we validated the validity of the six‐immDEGs risk signature in pure seminoma or mixed type of TGCT. The 24 immDEGs expression levels were significantly different between normal samples and seminoma or mixed type of TGCT Fig. . All semRORA group showed inhibition of NOTCH signaling pathways and type‐1 diabetes mellitus, while the VEGF signaling pathway and axon guidance were activated in this group . STC1 in TGCT was further characterized through bioinformatics and IHC experiments. First, the expression of STC1 was evaluated in TGCT datasets . As demonstrated in Fig. Although testicular cancer is a curable tumor, it is also associated with primary resistance, disease progression after therapy, and adverse effects of treatment. Identifying underlying molecular mechanisms and markers of poor prognosis is important for this subset of patients. The TME has been regarded as a critical factor in influencing tumorigenesis and progression , 25. In This study obtained 202 TCGT samples and 12 normal samples to identify immDEGs. A risk signature consisting of six immDEGs was constructed for TGCT for the first time. This signature was used to identify two immune molecular subtypes, namely Cluster 1 and Cluster 2. Prognostic differences were found to be significant between these two immune subtypes.et al. [TCF7L1, a WNT suppressor, can sensitively distinguish TGCT from nonseminomatous germ cell tumors [A total of 357 DEGs  and 88 DEGs (Cluster 1 vs. Cluster 2) were screened, respectively. The 24 immDEGs were identified in TCGT samples compared with normal samples. In the PPI network of two sets of DEGs, the 24 immDEGs were not closely linked and scattered throughout the network. This suggested that their functions might be relatively independent. In GO and KEGG analyses, DEGs between tumor and normal were mainly enriched in the reproductive process. Studies have shown that TGCT can disrupt the hypothalamus‐pituitary‐gonadal axis and lead to obstruction of sperm production, which can be manifested as decreased sperm quality or azoospermia , 30, 31.et al.  reportedl tumors . The TGFl tumors , 35. Imml tumors . It is wl tumors . The inhl tumors , 12, 13.l tumors . By the IGKC, IGLC1, STC1, ITGB2, CTSS, RORA, HLA‐DPB1, and RSAD2) were found to be related to OS of patients of TGCT via univariate Cox regression. However, the expressions of IGKC, IGLC1, ITGB2, CTSS, HLA‐DPB1, and RSAD2 were elevated in tumor tissue and they served as protective factors. Such conflicting expression levels and prognostic value are not uncommon in bioinformatics analysis of tumors. I think this can be explained by the following reasons. First, unlike single‐cell sequencing, in this study, we obtained the average transcriptome data of a population of cells, not just tumor cells. The changes in gene expression may be due to nontumor cells. Or the gene exerts biological effects through nontumor cell pathways. For example, Cao et al. [CXCL11 in colon cancer could improve the prognosis of patients by promoting antitumor immunity. Second, we measured the mRNA expression levels of genes only at one point in time. The level of gene transcription is regulated by many factors and changes rapidly. Third, mRNA is subject to post‐transcriptional regulation. The mRNA expression levels are not equivalent to protein expression levels. Proteins are the main bodies that perform biological functions. The protein expression levels of genes need to be further verified.A total of eight immDEGs  related to the OS of patients were identified via multivariate Cox regression by 24 immDEGs. In breast carcinoma, non‐small‐cell lung carcinoma, and colorectal carcinoma, IGKC has been linked to a favorable prognosis and may be utilized as a compatible prognostic indicator in human solid tumors [IGKC was significantly higher in ovarian cancer and clear cell renal cell carcinoma than in normal tissues [RORA, one of the circadian genes, inhibited tumorigenesis and progression in various tumors, including breast cancer [STC1 behaves as an oncogene in our results. Stanniocalcin‐1 (STC1) promoted different types of cancer progression [et al. [Except for c et al. , there wd tumors , 42. How tissues , 44. RORt cancer , 46, lunt cancer , 47, endt cancer , and gast cancer . Only STgression , 51, 52. [et al. , STC1 isA novel immunophenotyping of TGCT was presented. Based on six signature immDEGs, two immune molecular subtypes (Cluster 1 and Cluster 2) were identified, and Cluster 1 possessed an unfavorable OS compared with Cluster 2. Traditional histological classification has certain limitations, especially for high‐grade tumors, chemotherapy‐resistant tumors, and recurrent tumors. In patients with the same histological classification, there may be large differences in the efficacy of the same treatment. The fast advancement of molecular technologies, such as sequencing, has led to an increasing application of molecular subtyping in various types of tumors , 55, 56.et al. and Savelyeva et al. [Seminoma is the most common histologic subtype of TGCT in young men. Mixed type of TGCT is the most common type of nonseminoma. Pure embryonal carcinoma, teratoma, and yolk sac tumor are rare. Medvedev a et al. , 58 idenAlthough we got some meaningful results, there are some inherent limitations in our study. First, the study was based on single‐center data. This six‐immDEGs risk signature needs to be validated in more clinical cohorts. Second, we used a dataset that included seminoma, mixed type of TGCT, embryonal carcinoma, teratoma, and yolk sac tumor. Although we validated the six‐immDEG risk marker in pure seminoma and mixed type of TGCT, a different risk model might be obtained if one of the TGCT types were analyzed alone. Third, the genes identified in this study need to be experimentally verified to explore their mechanisms in TGCT.GSE3218, and clinical tissues, and its high expression level was linked to advanced clinicopathological features and poor prognosis. These findings contribute to a better understanding of TGCT.To summarize, bioinformatics analyses were conducted to compare immune infiltration in the TGCT and normal samples. A six‐immDEGs risk signature was constructed, and two immune molecular subtypes with significant prognostic differences were identified. WNT signaling pathway, TGF‐β signaling pathway, antigen processing and presentation, and NK cell‐mediated cytotoxicity were linked to TGCT. The expression level of STC1 was found to be elevated in TCGA, The authors declare no conflict of interest.ZZ contributed to writing—original draft, funding acquisition, and resources. XX contributed to conceptualization and writing—review and editing. XW and MW contributed to methodology, software, and formal analysis. CM contributed to resources and writing—review and editing. ZL contributed to conceptualization, software, funding acquisition, and writing—review and editing.Fig. S1. Sample distribution before de‐batching (A) and after de‐batching (B).Click here for additional data file.Fig. S2. Unsupervised clustering of the samples with all genes (A) or 22 immune cell types (B).Click here for additional data file.Fig. S3. Screening out six characteristic genes out of eight immDEGs using the LASSO algorithm. (A) LASSO coefficient path diagram. (B) LASSO regression analysis cross validation curve.Click here for additional data file.Fig. S4. Pan‐cancer analysis of STC1. ‐ P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; rank‐sum test. Tumor types (sample number): ACC ; BLCA ; BRCA ; CEST ; CHOL ; COAD ; DLBC ; ESCA ; GBM ; HNSC ; KICH ; KIRC ; KIRP ; LAML ; LGG ; LIHC ; LUAD ; LUSC ; MESO ; OV ; PAAD ; PCPG ; PRAD ; READ ; SARC ; SKCM ; STAD ; TGCT ; THCA ; THYM ; UCEC ; UCS ; UVM .Click here for additional data file.


Linear (p = .003) and quadratic (p = .046) increased effects were observed in milk fat as corn silage was replaced with sorghum silage. Compared with the CON diet group, the CS2 and CS3 diet groups had lower dry matter (DM) , ether extract (EE) , and gross energy (GE)  digestibility of the dairy cows. The ruminal fluid aspartate (Asp) level decreased  as the proportion of sweet sorghum increased. Linear (p < .05) and quadratic (p < .05) increased effects were observed for the contents of threonine (Thr), glycine (Gly), valine , leucine (Leu), tyrosine (Tyr), and histidine (His) in rumen fluid with the replacement of corn silage with sorghum silage. Cows fed the CS3 diet had greater Faecalibacterium, Bacteroides, and Prevotella ruminicola content/copy number than those fed the CON diet (p < .05). In conclusion, feeding sorghum silage as a replacement for corn silage could increase the milk yield and fat, promote the growth of rumen microbes, and provide more rumen fluid amino acids for the body and microbial utilization. We believe that sorghum silage is feasible for dairy cows, and it is reasonable to replace corn silage with 75% sorghum silage.This experiment explored the effects of different proportions of sweet sorghum silage as a substitute for corn silage on dry matter intake (DMI), milk yield, milk quality, apparent digestibility, rumen fermentation parameters, serum amino acid profile, and rumen microbial composition of dairy cows. A total of 32 mid‐lactation Holstein dairy cows with similar body weights and parities were randomly divided into four treatments: 100% corn silage +0% sorghum silage (CON), 75% corn silage +25% sorghum silage (CS1), 50% corn silage +50% sorghum silage (CS2), and 25% corn silage +75% sorghum silage (CS3). The milk yield was increased (linear,  Feeding sorghum silage as a replacement for corn silage could increase milk yield and fat. Sorghum silage is feasible for dairy cows, and it is reasonable to replace corn silage with 75% sorghum silage. Annual variations in climatic conditions  are often reasons for the limited amount of forage production in numerous regions of China. Corn silage has become the main forage feed for dairy cows in many areas due to its high DM yield, high energy concentration, and optimum DM concentration at harvest for good forage fermentation and storage  has been cultivated in many areas of China due to its drought and salinity tolerance, lower water requirements, and fertility requirements than corn , 3 kg of NaCl, and 2.5 kg of urea were evenly sprayed on each ton of silage after being dissolved in water. Silage samples were taken at four different points in the silage cellar, dried at 65°C, crushed, passed through a 40‐mesh sieve, and then stored in sealed plastic containers at 4°C until further analysis  430148. Sweet sorghum was planted in 2018 by Hunan Youzhuo Animal Husbandry Co., LTD, Changsha, 410,128, and harvested at the milk stage of maturity. Corn  was planted and harvested simultaneously during the milk stage of maturity. Sweet sorghum and corn are directly chopped and cut to a theoretical chopping length of 2–3 cm, then placed in the ground silage, compacted with a compactor machine, and covered with plastic sheets on top. Three grams of 2.2This research was conducted at Hunan Youzhuo Animal Husbandry Co., LTD, Changsha, 410,128 (North latitude N28°24′20.90″ east longitude E112°39′46.12″). The experimental period is from June to August 2019. All animals were cared for according to procedures approved by the Institute of Subtropical Agriculture, Chinese Academy of Sciences Ethics Committee. Thirty‐two mid‐lactation Holstein dairy cows with similar body weights (595 ± 40 kg) and parities (2 ± 1.2) were randomly divided into four treatments: 100% corn silage +0% sorghum silage (CON), 75% corn silage +25% sorghum silage (CS1), 50% corn silage +50% sorghum silage (CS2), and 25% corn silage +75% sorghum silage (CS3). The composition and nutritional levels of the four different diets are shown in Table 2.3The feed and residue of total mixed ration (TMR) were recorded daily and stored at −20°C. The DM content of TMR and residues were analyzed weekly, and the intake of each cow was calculated. Cows were milked three times daily, and the amount of milk produced for each cow at each milking was recorded. Daily milk production was recorded using the Afimilk system . Milk samples were collected on the 7th, 14th, 21st, and 28th days of the experimental period  to analyze milk composition. The collected milk sample was divided into two parts: one was supplemented with 2‐bromo‐2‐nitro‐1,3‐propanediol and used for milk fat, protein, and lactose determination; the other was frozen at −20°C, and the urea nitrogen in milk was determined. The milk fat, protein, lactose, total solids (TS), and nonfat solids (SNF) concentrations were determined on an infrared milk analyzer . The yield of 4% FCM (kg/d) was calculated , ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) according to the Association of Official Analytical Chemists  were collected eight times over 3 days at (day 1) 05:00, 12:00, and 18:00 h; (day 2) 00:00, 09:00, 15:00, and 21:00 h; and (day 3) 0300 h to obtain a representative sample of a 24‐h period. Fecal samples from each cow were composited by combining approximately 100 g of feces from each time point. Fecal samples were divided into two portions, with one portion being treated with 10% H2.6g at 4°C for 15 min to obtain the serum. The collected serum was placed at −20°C until further analysis. The free amino acid profiles of plasma samples were determined according to the method described by Zhu et al. . Approximately 200 mL of the initial collection of rumen fluid was discarded. After the initial volume was discarded, an additional 500 mL of rumen fluid was collected for further analysis. The volatile fatty acids (VFA) and NHn et al. . Microbin et al. . One mil2.8According to the manufacturer's instructions, total DNA was extracted from the rumen fluid samples using the QIAamp®Fast DNA Stool Mini Kit . The quality and quantity of DNA were measured using a NanoDrop ND1000 . The copy numbers of bacterial DNA genes were quantified using an ABI 7900 Sequence Detection System  as detailed in a previous study  is the fixed effect of treatment, eij is the random residual error, and animals were considered random effects.For nutrient digestion, data were analyzed using the PROC MIXED of SAS . The model used for data analysis is as follows:Yijk is the dependent variable, μ is the overall mean, Ti (i = 1–4) is the fixed effect of treatment, Sj is the sample time, TSij is the interaction effect between Ti treatment and Sj sample time, and eijk is the random residual error. Sample time was considered a repeated measure, and animals were considered a random effect. Orthogonal polynomial contrasts were used to analyze the linear and quadratic effects of sorghum silage levels. The IML procedure of SAS  was conducted to correct the contrast coefficients of the orthogonal polynomial.For milk production and composition, rumen fermentation parameters, and AA profiles, the following model was used:p value <.05 was considered to indicate a statistically significant difference.Differences in rumen microbial population among four treatments were analyzed using the Kruskal–Wallis test, followed by multiple comparisons. A 33.1p < .001 and quadratically, p = .043). As the proportion of sweet sorghum increased, the milk yield increased . Linear (p = .003) and quadratic (p = .046) increased effects were observed in milk fat as corn silage replaced sorghum silage. Linear (p = .024) and quadratic effects (p < .001) were observed, with an increase in the 4% FCM yield when the sweet sorghum replaced the corn.The effect of various substitution ratios of sweet sorghum silage on the milk quality of dairy cows is shown in Table 3.2p < .001). Compared with the CON group, the DM digestibility in the CS3 group was reduced by 4.50% (p < .05). A quadratic effect (p = .001) was observed on the apparent digestibility of ADF as sorghum silage was replaced for corn silage. Cows fed the CS2 and CS3 diets had lower GE  and EE digestibility  than those fed the CON and CS1 diets.The effects of replacing corn silage with different proportions of sorghum silage on the apparent digestibility of nutrients in cows are shown in Table 3.3p < .001) decreased. A decreased effect  was observed for the proportion of propionate with the replacement of corn silage by sorghum silage. Cows fed a diet with high levels of sweet sorghum had a higher proportion of valerate . The acetate: propionate (A:P) was decreased quadratically (p = .007) as the substitution ratio of the sweet sorghum increased. The content of ruminal TVFA was increased  with increasing sweet sorghum in the diet. Increased effects  were observed for the contents of butyrate, isobutyrate, and isovalerate with the replacement of corn by sweet sorghum. As the proportion of sweet sorghum increased, the content of NH3–N decreased .Table 3.4p = .003), and the treatment CS3 was significantly lower than other treatments. In contrast, the levels of serine (Ser), isoleucine (Ile), phenylalanine (Phe), proline (Pro), and total amino acids (TAA) increased linearly (p < .05) as the sweet sorghum proportion increased. Adding sweet sorghum to the diet increased  the contents of Thr, Gly, Val, Leu, Tyr, and His in rumen fluid. Neither alanine (Ala) and methionine (Met) nor lysine (Lys) (p > .05) were affected by the treatments.Table 3.5p = .001) content as corn silage was replaced with sorghum silage. The level of NH3L increased  as the proportion of sweet sorghum increased. Linear (p < .001) and quadratic (p = .032) increased effects were observed for the content of serum Asp with the replacement of corn by sweet sorghum silage (Table p < .05) (Table p > .05) (Table Table ge Table . With th5) Table . The con5) Table .3.6Faecalibacterium, Prevotella, and Prevotella ruminicola than those fed the CON diet (p < .05). The methanogen content in the CS1 group was significantly reduced compared with that in the CON and CS3 groups (p < .05). However, the contents of the general bacteria, Ruminococcus albus, Fibrobacter succinogenes, Selenomonas ruminantium, Streptococcus bovis, and Ruminobacter amylophilus were not altered by the different diets (p > .05).Table p < .05) with the rumen fluid Leu, Ile, Phe, Pro, Thr, Gly, TAA, Ser, and Tyr. However, there was a significant positive correlation (p < .05) between the serum Asp and the rumen fluid Val, Leu, Ile, Phe, Pro, Thr, Gly, TAA, Ser, Tyr, and His.Figure 44.1Due to the high yield of sorghum and its strong ability to adapt to drier environments, many scholars are devoted to exploring sorghum as a substitute for corn silage in dairy production  and ammonia, which can stimulate the growth of rumen bacteria . The bacterial copy number results also showed that the abundance of protein‐degraded bacteria, [e.g., Bacteroides  were profoundly decreased in the cow fed a diet with sorghum silage. In this regard, Leu declined by 22.34% (CON vs. CS2) and 38.54% (CON vs. CS3), and Val decreased by 24.98% (CON vs. CS2) and 31.70% (CON vs. CS3). Previous research claims that serum BCAA content is a good indicator of AA absorption ; formal analysis ; writing – original draft . Liang Chen: Data curation ; formal analysis . Chuanshe Zhou: Conceptualization ; funding acquisition ; writing – review and editing . Guijie Zhang: Data curation . jingjing xie: Data curation ; formal analysis . Jinhe Kang: Writing – review and editing . Zhi Liang Tan: Conceptualization . Shaoxun Tang: Writing – review and editing . Zhiwei Kong: Data curation ; formal analysis . Zixin Liu: Data curation ; formal analysis . Zhiyan Du: Data curation .This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA27040306); STS Project of the Chinese Academy of Sciences (CAS‐KFJ‐STS‐QYZD‐168); Major Special Project of Tibet Autonomous Region Science and Technology Plan (XZ202101ZD003N); and Science and Technology Department of Tibet (2020WK4002).The authors declare that no conflict of interest exists.The study received the approval of the Institutional Animal Care Committee, and all procedures involving animals were conducted following the guidelines on animal care of the Institute of Subtropical Agriculture, Chinese Academy of Sciences.


The peripartal period is marked by alterations in calcium metabolism to accommodate for embryonic skeletal mineralization and support bone development of offspring in early life, and serotonin plays a critical role in modulating peripartal bone remodeling. Selective serotonin reuptake inhibitors (SSRIs) are commonly used as first‐line treatment for psychiatric illness during pregnancy and the postpartum period and considered safe for maternal use during this time frame. In order to evaluate the effect of peripartal alterations of the serotonergic system on maternal skeletal physiology, we treated dams with the SSRI fluoxetine during gestation only, lactation only, or during the entire peripartal period. Overall, we found a low dose of fluoxetine during gestation only had minimal impacts on maternal bone at weaning, but there were implications on maternal skeleton at weaning when dams were exposed during lactation only or during the entire peripartal period. We found that these effects were differential between female mice dosed lactationally or peripartally, and there were also impacts on maternal mammary gland at weaning in both of these groups. Though SSRIs are largely considered safe maternally during the peripartal period, this study raises the question whether safety of SSRIs, specifically fluoxetine, during the peripartal period should be reevaluated. Until the first day of pregnancy, at which point they were housed separately, mice were housed in groups in an environmentally controlled facility for biological research in the Biochemistry department vivarium at the University of Wisconsin–Madison. Mice were maintained at a temperature of 25°C and a humidity of 50%–60% on a 12‐h light/dark cycle with food (Envigo‐Teklad #2018) and water access ad libitum. Eight to ten mice/group were enrolled per group for the assessment of the various outcomes measured. Two‐way ANOVA with a repeated measures design, and a Tukey's multiple comparison test were used to analyze serum calcium, serotonin, PTHLH, C‐terminal telopeptide fragments of type‐I collagen crosslinks, homocysteine, and milk yield to compare treatments over time. One‐way ANOVA with a Tukey's post hoc test was used to compare all measurements at weaning, and bone parameters at individual time points. With 10 animals per group, we have 80% power with an alpha of 0.05 to detect a significant difference of 3‐fold changes in gene expression/metabolite concentrations with a standard deviation of 1.2 between groups.Beginning at 6 weeks of age, female mice were mated overnight with a male C57BL/6 mouse also obtained from Jackson Laboratories. Pregnancy was determined by the presence of a vaginal plug, at which point the female mice were housed individually. On the first day of pregnancy (E0), mice were randomly assigned to receive sterile saline or 2 mg/kg body weight of fluoxetine hydrochloride  reconstituted in sterile saline daily. When accounting for physiological differences between rodents and humans, this dose approximates a 60 kg human taking 10 mg of fluoxetine per day  or 2 mg/kg fluoxetine (n = 17) daily from E0 through the end of pregnancy. The dams were then either collected at weaning  or were aged out to 3 months postweaning .The first experiment was designed to examine the effect of fluoxetine administration during gestation only, in order to determine the gestational effects of SSRI treatment. On the first day of pregnancy, the dams were randomly assigned to receive either sterile saline (2.1.2n = 17) or 2 mg/kg fluoxetine (n = 18) daily from the first day of lactation through D21. The dams were then either collected at weaning  or were aged out to 3 months postweaning .The second experiment was designed to examine the effect of fluoxetine administration during lactation only, and determine the effects of SSRI during lactation only. On the first day of lactation, the dams were randomly assigned to receive either sterile saline (2.1.3n = 16) or 2 mg/kg fluoxetine (n = 19) daily from E0 through D21. The dams were then either collected at weaning  or were aged out to 3 months postweaning .The final experiment was designed to examine the effect of fluoxetine administration during the entire peripartal period, from the beginning of gestation through the end of lactation. On the first day of pregnancy, the dams were randomly assigned to receive either sterile saline  via a PIXImus2 Mouse Densitometer  as previously described , trabecular thickness (Tb.Th), trabecular number (Tb.N.), trabecular separation (Tb.Sp.), midshaft bone volume fraction (M.BV/TV), and cortical thickness (C.Th) were calculated with software supplied by the manufacturer and were reported according to consensus guidelines on rodent micro‐CT  using a Scanco Medical μCT 35 system and integrated Scanco reconstruction software with an isotropic voxel size of 7 μm. Scans were conducted in 70% ethanol using an X‐ray tube potential of 55kVp, an X‐ray intensity of 0.145 mA, and an integration time of 400 ms. Digital calipers were used to measure femoral length. Cancellous bone analysis was measured via a selected region beginning 0.14 mm proximal to the growth plate and extending 1.4 mm proximally. Cortical parameters were calculated via a selected region centered at the midpoint of the femur and 0.6 mm in length. Cortical and trabecular bone were distinguished via a semiautomated contouring approach. The region of interest was selected using a global threshold that set the bone/marrow cutoff at 512 mgHA/cm2.5Serum serotonin concentrations were measured using the Beckman Coulter Enzyme Immunoassay Kit  per the manufacturer's instructions. Serum samples were diluted 1:200 to fit within the standard curve. Serum calcium concentrations were measured via Cayman Chemicals Calcium Assay Kit  per manufacturer's instructions. Samples were diluted 1:2 to fit within the standard curve. Serum procollagen I intact N‐terminal (P1NP) concentrations were measured via Immunodiagnostics Systems enzyme immunoassay  per manufacturer's instructions. Samples were diluted 1:10 to fit within the standard curve. Serum collagen type 1 cross‐linked C‐telopeptide (CTX) concentrations were measured via RatLaps™ (CTX‐I) Immunodiagnostics Systems enzyme immunoassay  per manufacturer's instructions. All assays had an intra‐assay CV of <15% and interassay CV of <15%.2.6Rps15, Rps9, K8, and K14. In the femur, the housekeeping parameter was the geometric mean of Rps15 and Hprt1. Analysis was conducted using the 2−ΔΔCT method  and RNA was reverse transcribed 1 μg) to cDNA via the Applied Biosystems High Capacity cDNA Reverse Transcription Kit . Quantitative RT‐PCR was performed using the CFX96 Touch Real‐Time PCR Detection System , and reaction mixtures and cycling conditions were performed as previously described . Sections were also deparaffinized and processed for immunofluorescence with the following antibodies: TPH1  and PCNA . The secondary antibodies used were Alexa Fluor 594 Goat Anti‐Rabbit IgG  and Alexa Fluor 488 Goat Anti‐Mouse IgG  and were incubated for 1 h at room temperature. DAPI, dilactate   was used to visualize nuclei. All images were captured via QuPath‐0.3.2 software on a Zeiss Axio Vert. One microscope at 20x.2.8t‐test. When data were not normally distributed, a Kruskal–Wallis test was performed for nonparametric data. Analyses with multiple time points were conducted using a two‐way ANOVA with Tukey's multiple comparisons test to detect differences between treatment groups. Outliers were determined using the Prism ROUT method analysis. For analyses, differences among means were considered significant if p < 0.05 or a tendency if 0.05 < p < 0.1. All values are reported as mean ± SD.All statistical analyses were conducted using GraphPad Prism 9 (Version 9.5.1). Analyses between the treatment groups without the effect of time were performed using a Student's unpaired two‐sided 33.1In order to determine the impact fluoxetine administration during gestation had on maternal bone, circulating markers of bone remodeling were measured in the dam serum at the end of lactation. There were no significant differences in circulating P1NP, CTX, or the P1NP/CTX ratio Figure . At the p < 0.0001) and a tendency for there to be an increase in the FLX animals compared to the controls (p < 0.1). The periosteal perimeter was decreased at 3 MO compared to D21 (p < 0.0001), and there was a significant interaction between treatment and time (p < 0.05), but no effect of treatment. The BMD and TMD of the cortical bone was increased at 3 MO compared to D21 (p < 0.0001), but there was no significant effect of treatment, time, or the interaction between the two variables in either the cortical area or length. In the trabecular bone, there was a significant decrease in bone volume/total volume (p < 0.0001), trabecular number (p < 0.0001), BMD (p < 0.0001), and connectivity density (p < 0.0001), a significant increase in the spacing of the trabeculae (p < 0.0001) and TMD (p < 0.0001), and a tendency for an increase in trabecular thickness (p < 0.1) at 3 MO compared to D21. There was a tendency for an interaction between time and treatment in trabecular number (p < 0.1), as well as a tendency for an increased trabecular number at D21 and decreased at 3 MO in the FLX animals compared to the controls (p < 0.1). Finally, there was an overall effect of the interaction between time and treatment on the trabecular TMD (p < 0.1).Micro‐CT analysis was used to investigate the characteristics of the cortical and trabecular bone Table . In the 3.2p < 0.0001), but tended to have less total tissue mass (p < 0.1)  Figure . Next, t) Figure . The wei3.3Trap, Mcp1, and M‐csf) and Mmp13, which is relevant to bone formation . Expression of Trap was downregulated in FLX dams (p < 0.001), whereas Mcp1 was upregulated (p < 0.001) and M‐csf was unaffected. The expression of Mmp13 was significantly downregulated in the FLX dams as well. To look at the ratio of bone breakdown to bone building in terms of gene expression at the level of the femur, the Rankl/Opg ratio was evaluated and was significantly decreased in the FLX animals (p < 0.05)  and CTX was increased (p < 0.05) in the FLX animals. The ratio of circulating markers of bone formation to bone breakdown was measured via the CTX/P1NP ratio, which was increased in the FLX animals (p < 0.01)  Figure . To furt) Figure . Serum l) Figure . The cha) Figure . There w) Figure , where tp < 0.0001), BMD (p < 0.0001), TMD (p < 0.0001), and length (p < 0.0001) in the 3 MO cohort compared to the D21, and there was a tendency for an increase in cortical area (p < 0.1). There was also a significant decrease in the periosteal perimeter (p < 0.05) associated with age. In the trabecular bone, there was a decrease in BV/TV (p < 0.0001), trabecular number (p < 0.0001), BMD (p < 0.0001), and connectivity density (p < 0.0001) associated with age. Further, there was also a significant increase in trabecular spacing (p < 0.0001) and TMD (p < 0.0001) in the 3 MO animals compared to the controls. There was also a significant association between FLX treatment and decreased TMD (p < 0.001).To further examine the short‐ and long‐term impacts of fluoxetine administration during lactation only on the maternal skeleton, micro‐CT was performed on the femurs of the dams at weaning and 3 months postweaning . There were no differences in expression of Shh or Gli1, which are both important members of the serotonin‐dependent signaling pathway that modulates lactational bone remodeling. M‐csf, which is important in mammary gland remodeling and milk production during lactation, was upregulated in the FLX mice (p < 0.05). Further, Tnfα and Ccnd1 were also upregulated in the FLX mice . Both Tnfα and Ccnd1 are associated with mammary epithelial proliferation. Finally, Casp3, which is associated with mammary involution, was unchanged. There was no difference in serum serotonin at any point during lactation, nor was there any change in circulating serotonin relative to the end of pregnancy between either group , but it was not significantly different  and bone formation (Mmp13) . Further, Trap was highly upregulated in the FLX animals (p < 0.0001), whereas Mcp1 and M‐csf were downregulated . There was no significant difference in Mmp13 expression. There was a significant increase in the Rankl/Opg ratio in the FLX animals compared to the controls (p < 0.0001)  and D21 (p < 0.01). Similarly, the change in BMD of the total body was also increased at D10 (p < 0.05) and D21 (p < 0.05). To determine the long‐term effect of 2 mg/kg FLX during gestation and lactation, the change in BMD at 3 months postweaning relative to the end of lactation was measured  Figure . Interes) Figure . The cha) Figure . The chad Figure . There wp < 0.0001), cortical area (p < 0.01), BMD (p < 0.0001), TMD (p < 0.0001), and length (p < 0.0001) between the D21 and 3 MO groups, and a significant decrease in the periosteal perimeter (p < 0.01) also associated with time. However, there are no significant differences in terms of treatment or the interaction between treatment and time in the cortical bone. In the trabecular bone, there is a significant increase in trabecular spacing (p < 0.0001) and TMD (p < 0.0001) between the two time points, as well as a significant decrease in BV/TV (p < 0.0001), trabecular number (p < 0.0001), BMD (p < 0.0001), and connectivity density (p < 0.0001). Further, there was a significant increase in TMD (p < 0.001) in the FLX dams at D21, along with a significant interaction between time and treatment (p < 0.01).To analyze the effect of fluoxetine administration during both gestation and lactation on the maternal skeleton, micro‐CT was performed on the femurs of the dams at both D21 and 3 months postweaning Table . In the 3.6p < 0.05). There was no significant difference in the circulating serotonin at the end of lactation, but the FLX dams tended to have a decreased circulating serotonin compared to the control dams (p < 0.1)  but there was no difference in the calcium transporter Pmca2 , but Pthlh remained unaffected. Finally, Casp3 was downregulated in the FLX dams compared to the control dams (p < 0.001). The mammary gland was further examined via visualization with H&E staining and immunofluorescence  Figure . There w) Figure . In the 2 Figure . Both Tpe Figure . The 2 m4In this study, we have demonstrated that administration of a low dose of fluoxetine has differential effects on the dams based on the window of administration. Overall, there were minimal effects on bone at weaning and 3 months postpartum in the dams that were exposed during gestation only. In humans, 80% of the calcium accrual in the fetal skeleton happens during the third trimester, and in rodents, the majority of fetal skeletal mineralization happens in the last few days of gestation Comar, . PregnanIn the dams exposed during lactation, there were alterations in femur gene expression, as well as markers of bone remodeling. Despite this, there were minimal effects on the cortical and trabecular bone at weaning and 3 months postweaning. During lactation, calcium absorption is the same as during a homeostatic state, and so most of the calcium provided to the offspring is sourced from the skeleton Kovacs, . HoweverRankl, which, in turn, decreases the RANKL/OPG ratio (Ardeshirpour et al., Rankl. Instead, Opg expression was what was primarily altered, which led to the differential RANKL/OPG ratio between the two fluoxetine‐treated groups at weaning.When considering the effects of fluoxetine administration, specifically on bone, the length of dosing is important. Of note, Ortuño and colleagues demonstrated that administration of 20 mg/kg fluoxetine to male and nonpregnant, nonlactating female mice resulted in net bone loss in a chronic model of fluoxetine use; however, in an acute model, an antiresorptive effect was observed (Ortuño et al., Tph1 expression. The dams that were dosed throughout the entire peripartal period also had upregulation of Tph1 expression in the mammary gland, but the change in circulating serotonin was increased at peak lactation. Interestingly, the mammary glands in the dams treated in both gestation and lactation appear to have increased adipose and distended alveoli compared to the control treated dams. Additionally, it appears that there is more robust staining of PCNA in the control treated dams compared to dams treated with FLX. These data suggest that dams treated with SSRI could potentially undergo weaning more rapidly. This coincides with the decreased circulating serotonin concentrations that were observed at weaning only in these dams as well, an indicator of SSRI effectiveness. This is also consistent with previous studies indicating that increased SSRI treatment can break down tight junctions, leading to a hastening of mammary gland involution, even at a more modest dose of FLX (Marshall et al., Shh gene expression. Our previous data in which we observed both increased Shh and Pthlh gene expression and reduced gene methylation were at higher doses of FLX that suppressed circulating serotonin concentrations much earlier, as well as much more robust induction of serotonin signaling (Laporta et al., In the mammary gland, there were alterations in gene expression in the lactation and the gestation and lactation animals, and changes in gene expression were different depending on whether the dams were dosed postpartally or peripartally. Finally, there was an effect on circulating serotonin at peak lactation in the animals dosed during pregnancy and lactation, but that was not recapitulated in the dams dosed during lactation only. In the dams that were only dosed during lactation, there was an upregulation of mammary gland Tph1 and Shh gene expression were increased unlike in previous studies, the mammary glands in the FLX‐treated dams appeared to have increased adipose tissue and reduced PCNA at weaning than controls. Future studies should focus on the mammary gland at different phases of lactation to evaluate the effect of fluoxetine more fully during the peripartal period at a lower dose.There are several limitations of this study that warrant discussion. First, we did not examine different doses of fluoxetine, nor did we analyze any other SSRIs. Previously, we have conducted similar studies using a high dose of fluoxetine or sertraline, another member of the SSRI class of antidepressants, but these studies only included dosing during the entire peripartal period (Sheftel et al., In conclusion, a low dose of fluoxetine during gestation, lactation, or the peripartal period did not have long‐term structural effects on the dam skeleton, contrary to our previous work conducted with a high dose of fluoxetine (Weaver et al., H.P.F., J.F.C., and L.L.H. conceived of the experiments, analyzed, and interpreted the results. L.A.W. and J.F.C. analyzed bone micro‐CT data. C.J.K, M.J.P., M.A.R., and L.J.B. analyzed images and collected data. All authors read and approved manuscript.This work was funded by NICHD R01HD094759 to Laura L. Hernandez. Hannah P. Fricke was funded by a T32HD041921. Julia F. Charles is funded by NIAMS R01AG046257 and R21AR07768.The authors have nothing to disclose.All experiments were approved by the Research Animal Care and Use Committee at the University of Wisconsin–Madison and were performed under protocol number A005789‐R01‐A03.