Oligomenorrhea

Most eye-tracking software provide raw gaze data, with the following variables that are critical for the present analyses: (1) x- and y-coordinates for the point of gaze on the screen (separately for each eye), sampled at the specified temporal resolution (60–300 Hz in most eyetrackers used with infants), (2) time stamps for each data sample (e.g.,“Tobii Eye Tracking or “TETTime” provides the time stamps at microsecond accuracy), (3) information about the “validity” indicating the reliability of tracking at each time point (e.g., Tobii TX300 uses codes 0–4, with codes 0 or 1 typically considered to indicate technically reliable gaze tracking), and (4) additional time stamps to provide exact synchronization between eye tracking and stimulus presentation (e.g., a column specifying the stimulus that is currently on screen). The x-coordinates of the gaze location for one overlap SRT trial of a 7-month-old participant are shown in Fig. 2 (the y-coordinates were omitted from the visualization because these tend to remain relatively stable across time in paradigms in which the first and the second stimuli are aligned on the vertical axis). The visualization illustrates two common characteristics of eye-tracking data collected from infants (Wass et al., 2013 (link), 2014 ). First, the raw data includes occasional periods of missing or unreliable data (shows as gaps in the thick red line at the y = 0). Second, the point of gaze undergoes constant fluctuation at periods of fixation (a problem known as low precision of eye tracking). The visualization further shows that the x-coordinates show an abrupt change at the time of the saccade.Fig. 2

X-coordinates of gaze location as a function of time for one trial of a 7-month-old infant. The data were recorded in a paradigm involving a central stimulus (a picture of a face or a facelike pattern) and a lateral stimulus (a geometric shape). The lateral stimulus was presented at 1,000 ms. Raw values for the point of gaze are shown by the narrow green line, and interpolated and median-filtered values by the thick blue line. Saccade is indicated by an abrupt change in the x-coordinates ~1,700 ms from the start and is measured as the last sample before the point of gaze leaves the area of the first stimulus (indicated by an open circle)

Raw top-view movies were processed with custom MATLAB code. For RGB movies, the 80th percentile of a manually or automatically selected set of frames was used as background that was subtracted from all the frames. Then both for RGB and thermal movies, a threshold was manually selected on a GUI, and the resulting binary mask underwent a series of simple treatments: morphological closing, removal of small objects, another morphological closing and finally morphological opening. The values for the different parameters were set to accommodate the different conditions. For each frame, mice contour and center of gravity were obtained from the resulting mask and saved. A calibration (px_movie / cm_{real object} ratio) was also obtained from a manually drawn segment and the corresponding length of the object in cm, for later normalization. In the following analysis steps, speed and mouse position were derived from the center of gravity coordinates, which were slightly smoothed with a median filter. In addition, to capture general activity, even in the absence of locomotion, a motion measure was used: it is computed as percentage of pixel change in the mouse masks from one frame to the next (nonoverlapping pixels/total pixel count). Several body parts (snout, ears, front and hind paws and tail) were also tracked with python-code-based DeepLabCut^{52 (link),53 (link)}. Briefly, a Resnet-152 network was iteratively trained and refined on ~1,350 frames to be as performant as possible in all our various recording conditions and in particular yield accurate tail tracking (cf thermal data extraction). To not sacrifice any accuracy, only coordinates with a score ≥0.99 were included, and no interpolation was applied. A semi-automated threshold-based GUI was used to annotate the following behaviors: rearing, grooming, stretch-attend posture, head dips, immobility, fast locomotion and so-called area-bound (not any of the other defined behaviors, in particular, no immobility and no locomotion). Briefly, for each behavior, a global score was obtained from relevant position information, body parts’ angles/distances/speed and thresholded with their respective hard-coded thresholds. The behavioral bouts resulting from that initial detection were displayed in a GUI together with the original movie and the scores. The thresholds could then be dragged manually, updating the detected events plots, to get the best possible detection. Events were then checked and adjusted manually when needed within the same GUI, and occasional periods of obstruction (for example, cable between the camera and the mouse) were marked for later exclusion. In the specific case of the LDB, because the RGB camera was not able to capture the mouse’s activity in the dark side, thermal movies were used for behavioral detection. To this end, thermal movies were re-exported with a black-and-white color map, after inverting the intensities and adjusting the contrast, so that the resulting frames resemble their RGB counterparts. A DeepLabCut network was derived from our main network and refined with those new movies (tail points were discarded because of their changing nature on thermal movies). The tracked body parts were then used as previously mentioned to detect behaviors. Mouse tracking was performed blind to the conditions of the experiments.

Infertile PCOS patients aged 18–40 years were enrolled in this randomized clinical trial (RCT). They all met the Rotterdam criteria^{42 (link)} and were advised to undergo intracytoplasmic sperm injection (ICSI). PCOS was the only endocrinopathy in all of them and oligomenorrhea with oligo-ovulation due to the higher prevalence rate was the main reason for the inclusion of these patients.
Patients were excluded from the trial if they fulfilled any of the following criteria:
Severe endometriosis (stages 3 and 4 in terms of the revised AFS-rAFS classifying the endometriosis), FSH > 10 mg/mL, hyperprolactinemia, Cushing’s disease, ovarian tumors, thyroid disease, severe male factor infertility (particularly non-obstructive azoospermia), drug history affecting ovarian function in the 3 months prior to the study (steroids and oral contraceptive pills [OCPs]), female infertility factors other than cervical and tubal factors, any autoimmune disease, systemic disorders like metabolic syndrome, severe obesity and malnutrition (body mass index [BMI] over 35), hyperlipidemia, diabetes, and cardiovascular disease.
The participants were recruited from patients who were candidates for the ICSI protocol at Omid Clinic, Tehran, Iran, between November 2020 and September 2021. Although male factor indication for ICSI utilization appears to be constant, non-male factor indications remain controversial^{43 (link)}. Some have advocated for the universal application of ICSI to all oocytes, regardless of the cause of infertility^{44 (link),45 (link)}. Moreover, several studieshave shown that conventional insemination of defective oocytes does not result in fertilization, whereas the use of ICSI increases fertilization and improve clinical outcome^{43 (link),46 (link)}. On the other hand, previous studies have shown that oocyte quality is low in patients with PCOS^{47 (link),48 (link)}. In our center, it has also been seen that PCOS patients with more dysmorphic oocytes have a lower fertilization rate after in vitro fertilization (IVF) compared to ICSI; accordingly, we employed ICSI, even though there was no male factor. The Ethics Committee of Tehran University of Medical Sciences approved the project (code:IR.TUMS.REC.1399.340) in accordance with relevant guidelines/regulations. The study was registered on the Iranian Registry of Clinical Trials (IRCT) website (IRCT-ID: IRCT20201029049183N1). Only a part of the results of the clinical trial registered with IRCT is presented in this paper. Before the intervention, each participant signed the informed consent form.

Participants were asked to report the ‘average number of alcoholic drinks per week’ that they consumed (answer possibilities: 0 to >100) and ‘the number of drinking days per week’ (answer possibilities: 0 to 7 days). Guidance was provided on serving sizes and how to convert these into standard alcoholic drink sizes (units). For liquor and mixed drinks, one shot equaled one unit. One glass of beer (250 mL), and one glass of wine, counted as one unit. One bottle of wine (750 mL) equaled 6 units, and one bottle of liquor (750 mL) equaled 20 units. The items were rated for (1) the period before the COVID-19 pandemic, (2) the first lockdown period (March–December 2020), (3) summer 2021 (January–March 2021, no lockdown), and (4) the second lockdown (April–July 2021). Regarding the heaviest drinking occasions in these time periods, participants reported the number of alcoholic drinks they consumed on such occasions. Participants rated their drunkenness (i.e., subjective intoxication) on a scale ranging from 0 (sober) to 10 (extremely drunk) [17 (link)], and next day hangover severity was rated on a scale ranging from 0 (absent) to 10 (extreme) [18 (link)]. Participants also reported ‘the number of hangovers per month’ that they experienced (answer possibilities: 0 to 31 days) for the four time periods. For each period, participants reported how many days per week they smoked (answering possibilities: 0 to 7 days), and on average how many cigarettes they smoked per day (answering possibilities: 0 to >100).