You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#Sec1}
============

Since the first case of influenza A (H1N1) virus infection found in Mexico in 2009, the virus has spread worldwide and resulted in pandemics \[[@CR1]\]. Nearly 60.8 million were infected with H1N1from April 2009 to April 2010; of these, 274,304 were hospitalized and 12,469 died in the USA \[[@CR2], [@CR3]\]. The H1N1 infection was usually complicated by several diseases including acute respiratory distress syndrome, cardiac disorders, bacterial pneumonia and others. Pneumonia was one of the most common complications. Influenza and pneumonia mortality accounted for 2.2% and 2.0%, becoming the eighth and ninth causes of death in 2009 and 2010 in the USA, respectively \[[@CR4], [@CR5]\]. Recent studies also revealed that 18--61% of hospitalized patients with H1N1 infection had pneumonia characteristics, demanding more attention to influenza-induced pneumonia \[[@CR6]--[@CR8]\].

Previous studies have shown that severe pneumonia caused by influenza virus infection can be secondary to invasive pulmonary fungal (IPF) infection. It is probably because these people have predisposing factors, such as lung disease, malignancy and chronic obstructive pulmonary disease, affecting the function of T lymphocytes, macrophages and neutrophils, or influenza increases susceptibility to bacterial super-infection by altering defensive mechanisms \[[@CR9], [@CR10]\]. Once it occurs, treatment decisions change and the prognosis is greatly different \[[@CR11]\]. However, due to the limited case reports in recent years, the robust data on the prevalence, pathogenesis, risk factors and final prognosis of the aforementioned infection are sparse \[[@CR12]--[@CR14]\]. In a retrospective study published in 2012, the authors recruited 40 critically ill patients with H1N1 infection and nine of them developed invasive pulmonary aspergillosis, accounting for 23% \[[@CR15]\].

This study included 19 cases of severe pneumonia caused by influenza virus needing treatment and monitoring at the intensive care unit (ICU) in the last 4 months to provide more data for the H1N1-complicated IPF infection. In this retrospective study, the incidence of invasive fungal infection secondary to influenza virus was summarized and its etiology mechanism and high-risk factors were further explored (Fig. [1](#Fig1){ref-type="fig"}). Fig. 1Pathological examination of the airway mass from patients 6 and 7 revealed fibrous necrotic tissue and fungal filaments, which confirmed a fungal infection (× 100)

Materials and Methods {#Sec2}
=====================

Study Population {#Sec3}
----------------

This study was performed at the adult ICUs of Nanjing Drum Hospital. The medical records of all the patients with H1N1 virus infection during or 1 week prior to the ICU stay from November 2017 to March 2018 were retrospectively reviewed. The exclusion criteria were as follows: (a) age \< 18 or ≥ 80 years; (b) had primary immunodeficiency disease, autoimmune disease or malignancy; (c) received hormones or immunosuppressive agents for nearly 3 months or surgical intervention; and (d) died within 14 days of hospitalization.

The diagnosis of H1N1 virus infection was based on a positive result in a probe-based reverse transcription polymerase chain reaction test for H1N1 from a nasopharyngeal swab or bronchoalveolar lavage (BAL) fluid. This observational retrospective study without any specific intervention was reviewed and approved by the hospital's institutional review boards. All data were processed anonymously.

Definitions {#Sec4}
===========

IPF infection was confirmed following the revised European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) guidelines.

The patients had proven IPF infection if the microscopic evidence of dichotomous branching hyphae with a positive culture for fungi from endobronchial biopsy (not BAL) was obtained, irrespective of host factors or clinical features (Fig. [2](#Fig2){ref-type="fig"}).Fig. 2Time course of HLA-DR expression (%) in the IPF and no-IPF groups after admission

Probable IPF infection required a host factor, clinical features and mycological evidence of fungi. The mycological evidence was based on microscopy or culture of fungi from a BAL specimen or a galactomannan (GM) optical index (0.5 from a BAL or serum sample). The sandwich enzyme-linked immunosorbent assay for GM detection was performed according to the manufacturer's protocol. Lower tract respiratory samples were inoculated onto a Sabouraud agar (2 days at 37 °C and another 19 days at 30 °C) for fungal isolation. Fungal species were identified by their culture characteristics and morphologies.

The severity of illness was assessed using the acute physiology and chronic evaluation (APACHE) II score and the sequential organ failure assessment (SOFA) score after 24 h of admission.

The doses and frequency of all drugs administered in the month before and during admission to the ICU were updated in the medical records.

Biochemical data, including CD4 + /CD8 + ratio, percentage of natural killer (NK) and B cells and human leukocyte antigen--DR isotype (HLA-DR) in peripheral blood mononuclear cells, were recorded. Peripheral venous blood samples (2 mL) were obtained from patients on days 1, 3, 5 and 7 after admission using a strict aseptic technique. Biochemical data were determined using the BD Multitest Identification Mark Kit (BD Corporation, USA, state code 340503). The other reagents and equipment used were as follows: fluorescein isothiocyanate antihuman CD14 (eBioscience Corporation, USA, state code IM1638U), phycoerythrin (PE) antihuman HLA-DR (eBioscience Corporation), PE mouse immunoglobulin G (IgG)2b isotype control (eBioscience Corporation, state code IM0464U), FACSort flow cytometer (BD Corporation) and CellQuest software.

Statistical Analysis {#Sec5}
--------------------

The SPSS22.0 software was used for data analysis. The quantitative data with normal distribution and homogeneity of variance were expressed as mean ± standard deviation (SD) and compared using the independent-sample *t* test. The groups were compared using the nonparametric Wilcoxon test. The qualitative data were expressed as a percentage. The groups were compared using the chi-square test. The test level was 0.05.

Results {#Sec6}
=======

Patient Characteristics {#Sec7}
-----------------------

The baseline factors are listed in Tables [1](#Tab1){ref-type="table"} , [2](#Tab2){ref-type="table"}, [3](#Tab3){ref-type="table"}. A total of 19 adult critically ill patients with confirmed H1N1 infection were included in the study between November 2017 and March 2018. Table [1](#Tab1){ref-type="table"} shows that the patients had a mean age of 49.18 ± 14.38 years, and 13 patients (68.4%) were male. They were divided into two groups based on the occurrence of secondary fungal infections in the course of the disease: IPF and no IPF. The initial symptoms, body temperature, leukocyte count and neutrophil count were not different between the two groups (Table [2](#Tab2){ref-type="table"}). Also, no difference was observed in the onset to visiting time, aggravation time or ICU time (Table [3](#Tab3){ref-type="table"}). Four patients (23.5%) received corticosteroids before admission to hospital as therapy for "severe acute respiratory distress syndrome." All patients received antiviral therapies such as oseltamivir and peramivir. Although the initial antiviral time seemed longer in the IPF group (10.10 ± 2.29) than in the no-IPF group (6.86 ± 2.12), the difference was not significant (*P* = 0.272). On admission, the mean APACHE II score was 17.06 ± 6.81 and the median SOFA score was 5.35 ± 3.08, reflecting a high severity of illness and a high incidence of multiple organ dysfunction syndrome. During hospitalization, the worst oxygenation index of patients was 93.34 ± 30.57 mm Hg. The respiratory support methods included extracorporeal membrane oxygenation (ECMO), tracheal intubation, noninvasive ventilation, high-flow nasal cannulae and oxygen therapy.Table 1Demographic and clinical characteristics of the patients included in the studyTotal (*n* = 19)No IPF (*n* = 8)IPF (*n* = 11)*P*Age (year)49.2 ± 14.444.4 ± 12.252.5 ± 15.40.268Sex (male), n (%)13/19 (68.4%)4/8 (50%)9/11 (81.8%)*Contact history*None17/197/810/11Poultry2/191/81/11*Occupation*Farmer6/192/84/11Unemployed7/194/83/11Other6/192/84/11*Comorbid illness*Healthy10/194/86/11Hypertension5/191/84/11Liver disease4/194/80/11COPD (not taken systemic corticosteroids)1/191/80/11Diabetes mellitus2/190/82/11Coronary heart disease4/191/83/11Atrial fibrillation1/190/81/11Cerebellar hypoplasia1/191/80/11Cerebral infarction2/191/81/11Parkinson's disease1/191/80/11Pregnancy, *n* (%)3/19 (15.8%)2/8 (25%)1/11 (9.1%)Obesity (BMI ≥ 30 kg/m^2^), *n* (%)1/19 (5.3%)1/8 (12.5%)0/11 (0%)Worst oxygenation index (mm Hg)93.34 ± 30.57103.29 ± 24.0385.61 ± 34.140.265*Respiratory support*None, *n* (%)3/19 (15.8%)1/8 (12.5%)2/11 (18.2%)ECMO, *n* (%)5/19 (26.3%)3/8 (37.5%)2/11 (18.2%)ECMO, days9 (1--33)9 (8--10)17 (1--33)MV, *n* (%)5/19 (26.3%)2/8 (25%)3/11 (27.3%)NIV, *n* (%)5/19 (26.3%)2/8 (25%)3/11 (27.3%)Mechanical ventilation, days9 (0--45)10 (0--17)8 (0--45)HFNC, *n* (%)1/19 (5.3%)0/8 (0%)1/11 (9.1%)Vasopressors, *n* (%)8/19 (42.1%)3/8 (37.5%)5/11 (45.5%)Vasopressor (day)0.2 (0--40)0 (0--6)0.5 (0--40)Renal replacement therapy, *n* (%)2/19 (10.5%)0/8 (0%)2/11 (18.2%)Renal replacement therapy (day)0 (0--60)0(0)0 (0--60)APACHE II17.1 ± 6.819.0 ± 9.015.7 ± 4.890.341SOFA5.4 ± 3.14.4 ± 2.46.0 ± 3.20.316*APACHE* acute physiology and chronic evaluation score, *BMI* body mass index, *COPD* chronic obstructive pulmonary disease, *ECMO* extracorporeal membrane oxygenation, *HFNC* high-flow nasal cannula, *IPF* invasive pulmonary fungus, *MV* mechanical ventilation days, *NIV* noninvasive mechanical ventilation, *SOFA* sequential organ failure assessmentTable 2Initial clinical symptoms, previous treatment and prognosis of the patientsTotal (*n* = 19)No IFI (*n* = 8)IFI (*n* = 11)*PFirst symptoms*Cough19/198/811/11Expectoration12/195/87/11 Sore throat2/192/80/11 Wheezing or dyspnea2/191/81/11 Headache1/190/81/11Initial body temperature (°C)39.1 ± 0.739.0 ± 0.839.2 ± 0.6*0.58*Initial leukocyte (× 10^9^/L)6.9 ± 3.27.5 ± 3.96.3 ± 2.4*0.466*Table 3Previous treatment of the patientsTotal (*n *= 19)No IFI (*n *= 8)IFI (*n *= 11)*P*Onset to visiting time3.7  ±  5.93.1 ± 2.34.0  ± 7.5*0.777*Onset to aggravation time6.6  ±  5.25.0 ± 2.37.7 ± 6.4*0.306*Onset to ICU time9.1 ± 6.26.3 ± 2.911.0 ± 7.3*0.128*Use of glucocorticoids in the course of disease4/192/82/11Initial antiviral therapyOseltamivir15/195/810/11Peramivir4/193/81/11Time of initial antiviral8.8 ± 5.86.9 ± 2.110.1 ± 2.3*0.272*Time of virus clearance10.9 ± 7.812.3 ± 9.09.7 ± 6.9*0.514*Onset to visiting time: Onset from first influenza symptoms to outpatient visit.Onset to aggravation time: Patient\'s symptoms cannot be tolerated.Time of initial antiviral treatment: Onset of symptoms to start antiviral treatment.Time of virus clearance: Time for virus to turn negative \[The diagnosis of H1N1 virus infection was based on a positive result in a probe-based reverse transcription polymerase chain reaction test for H1N1 from a nasopharyngeal swab or bronchoalveolar lavage (BAL) fluid.\]

Patient Prognosis {#Sec8}
=================

The results are shown in Table [4](#Tab4){ref-type="table"}. In the present study, 15/19 (78.9%) of patients, 7 in the IPF group and 8 people in the no-IPF group, were still alive at ICU discharge and hospital discharge. The length of ICU stay and hospital stay was 25.29 ± 18.04 days and 25.94 ± 17.70 days, respectively. The two groups had no significant difference in the length of ICU stay or hospital stay (*P* = 0.293 or 0.200).Table 4Prognosis of the patientsTotal (*n *= 19)no IFI (*n *= 8)IFI (*n *= 11)*P*Alive at ICU discharge, n (%)15/19 (78.9%)7/8 (87.5%)8/11 (72.7%)*0.334*Alive at hospital discharge, n (%)15/19 (78.9%)7/8 (87.5%)8/11 (72.7%)*0.521*Length of ICU stay25.3 ± 18.024.6 ± 11.925.8 ± 22.0*0.293*Length of hospital stay26.0 ± 18.026.1 ± 10.525.8 ± 22.0*0.2*Onset to visiting time: Onset from first influenza symptoms to outpatient visit.Onset to aggravation time: Patient's symptoms cannot be tolerated.Time of initial antiviral treatment: Onset of symptoms to start antiviral treatment.Time of virus clearance: Time for virus to turn negative \[The diagnosis of H1N1 virus infection was based on a positive result in a probe-based reverse transcription polymerase chain reaction test for H1N1 from a nasopharyngeal swab or bronchoalveolar lavage (BAL) fluid.\]

IPF Infection {#Sec9}
=============

The study found that 57.9% (11/19) of the critically ill patients with H1N1 infection developed IPF infection during ICU stay (Table [5](#Tab5){ref-type="table"}). The time between diagnosis of influenza and IPF infection ranged from 4 to 12 days, with a mean of 7.3 days and a median of 7 days. On the basis of the modified EORTC/MSG criteria, IPF infection probably occurred in nine patients and was confirmed in two patients. Nine patients were treated with antifungal therapy. All of them were treated with voriconazole.Table 5Overview of all individual cases of IPF infectionPatientFungusGM blood\
(pg/mL)GM bloodGM BALDay of first indication of IPF infection after ICU admission (days)Diagnosis of IPF infectionDrug1*Aspergillus niger*, aflatoxin93.10.147ProbableVoriconazole2*CandidaAlbicans*104.80.3210ProbableVoriconazole3*Aspergillus*240.984ProbableVoriconazole4*Aspergillus*,*Candida albicans*111.30.513.912ProbableVoriconazole5*Aspergillus*97.70.176ProbableVoriconazole6*AspergillusFumigatus*1884.744.897ProvenVoriconazole7*CandidaAlbicans*301.22.315ProvenVoriconazole8*Candida kurou*170.066ProbableNone9*CandidaAlbicans*31.20.094ProbableNone10*CandidaAlbicans*312.60.310.9810ProbableVoriconazole11*Aspergillus*9.80.460.5110ProbableVoriconazole

Immune Function Monitoring {#Sec10}
==========================

Table [6](#Tab6){ref-type="table"} shows that the HLA-DR percentage on days 1 and 7 did not differ significantly between the groups. However, a difference in △HLA-DR (day 7--day 1) was found between the two groups (*P* = 0.019). Also, differences in the NK cell count were observed on day 1 between the two groups. The CD4 + /CD8 + ratio, percentage of B cells, percentage of neutrophils and percentage of lymphocytes did not differ significantly between the groups at any of the time points.Table 6Immune function monitoringTotal (*n* = 19)No IPF (*n* = 8)IPF (*n* = 11)*P*HLA-DR day 1 (%)51.2 ± 20.146.5 ± 14.954.5 ± 23.20.435HLA-DR day7 (%)47.1 ± 20.558.2 ± 16.338.76 ± 20.00.076△HLA-DR (day 7--day 1) (%)--0.50 ± 21.914.5 ± 14.2--11.7 ± 20.20.019CD4 + /CD8 + day 11.5 ± 0.61.3 ± 0.61.7 ± 0.50.293CD4 + /CD8 + day 71.7 ± 1.01.3 ± 1.01.9 ± 1.00.757△CD4 + /CD8 + (day 7 -- day 1)0.1 ± 0.80.0 ± 0.60.2 ± 1.00.711NK cell day 1 (× 10^9^/L)0.1 ± 0.10.1 ± 0.10.1 ± 0.10.950NK cell day 7 (× 10^9^/L)0.1 ± 0.10.2 ± 0.10.1 ± 0.10.372△NK cell(day 7 -- day 1) (× 10^9^/L)0.0 ± 0.10.1 ± 0.1--0.0 ± 0.10.066B cell day 1(× 10^9^/L)0.2 ± 0.10.2 ± 0.10.2 ± 0.10.997B cell day 7(× 10^9^/L)0.2 ± 0.10.2 ± 0.10.2 ± 0.10.776△B cell(day 7 -- day 1) (× 10^9^/L)0.0 ± 0.10.0 ± 0.20.0 ± 0.10.260Neutrophils day 1(× 10^9^/L)5.5 ± 3.45.4 ± 4.15.6 ± 3.00.414Neutrophils day 7(× 10^9^/L)8.0 ± 4.19.3 ± 4.47.1 ± 3.90.824△Neutrophils(day 7 -- day 1) (× 10^9^/L)2.6 ± 3.23.8 ± 4.01.5 ± 2.10.135Lymphocyte day 1(× 10^9^/L)0.6 ± 0.30.5 ± 0.20.7 ± 0.40.122Lymphocyte day 7(× 10^9^/L)1.1 ± 0.51.0 ± 0.51.2 ± 0.50.435△Lymphocyte (day 7--day 1) (× 10^9^/L)0.5 ± 0.50.5 ± 0.60.5 ± 0.40.168

Discussion {#Sec11}
==========

The intensive understanding of H1N1 virus, application of vaccines and improvements in treatment options have facilitated the entry into the post-pandemic period \[[@CR16]\]. Although patients with H1N1 infection in post-pandemic years were less serious than those in the pandemic year, some complications are still common, severe and fatal. A potential complication of influenza is super-infection with fungal species. An increasing number of cases have been reported over time \[[@CR17]--[@CR19]\].

Wauters et al. \[[@CR15]\] retrospectively selected all adult patients with confirmed H1N1 virus infection admitted to the ICU of the two tertiary care hospitals from September 2009 to March 2011. These 40 patients had an average APACHE II score of 23 ± 8, an average SOFA score of 11 and a high degree of disease risk. Of 40 critically ill patients with confirmed H1N1, 9 (23%) developed IPA after 3 days of ICU admission. In contrast, the present study focused on recruiting patients from the department within 4 months, indicating that the virus was more homologous and the differences between individuals diagnosed and treated were small. The average APACHE II score was 17.06 ± 6.81, and the SOFA score was 5.35 ± 3.08. The severity of the disease and organ failure appeared to be less than that reported by Wauter. However, further observations revealed that the patients in the present study had a relatively short period of time from onset to ICU admission, and the statistics included APACHE II scores and SOFA scores within 24 h of ICU admission. After ICU admission, the disease was still in a developing stage and did not reach the critical peak in 24 h. The lowest oxygenation index was 93.34 ± 30.57 mm Hg in the course of the disease. Also, 88% of patients opted for mechanical ventilation and 28% for ECMO. In the study by Wauter, 23.5% of patients opted for ECMO and 88.2% for mechanical ventilation. Hence, the patients in the two studies were quite similar in terms of criticality and disease characteristics. In the aforementioned study, the incidence of invasive fungal infection in the lungs was 9/40 (23%). In the present study, the incidence was 11/19 (58.9%). One possible hypothesis was the evolution of more virulent influenza strains, as exemplified by the pandemic H1N1 strain, causing more severe pneumonia. Compared with previous findings \[[@CR17]\], the average viral transposition time of the patients was longer \[(10.88 ± 7.81) days\] and the duration of the disease increased in the present study. This result also proved the previous hypothesis. A retrospective study found that the risk of IPF infection was higher when respiratory viruses were in circulation, in particular, influenza A \[[@CR20]\]. IPF infections were prone to occur during viral infection and replication process. The time between diagnosis of influenza and IPF infection in 11 patients was a mean of 7.3 days (SD ± 0.7). This result was almost consistent with previous findings \[[@CR21]\].

The data in the present study suggested that the use of corticosteroids 7 days before ICU admission was an independent risk factor for fungal super-infection. In this study, no-IPF group had prehospitalization rates of 28.6% and 20%. No significant difference was observed between the two groups, demonstrating no significant relationship between glucocorticoid use and secondary fungal infection before hospital admission. This result was not consistent with previous observations.

Therefore, other mechanisms leading to decreased immune function might exist. Influenza virus affects the production of cytokines, in turn impacting the immune system. H1N1 virus leads to an increased and prolonged production of interleukin-10 (IL-10) in plasma. This, in turn, affects Th2 response and adaptive immunity mediated by Th2, which is associated with an increased risk of IPF infection \[[@CR22]\]. The influenza virus has been reported to impair phagocytosis and alveolar macrophage function, increasing the risk of IPF infection \[[@CR9]\]. The severe systemic inflammatory syndrome, which is called compensatory anti-inflammatory response syndrome or immunoparalysis, may occur in patients with severe influenza infection, making them vulnerable to severe super-infections by bacteria and fungi \[[@CR10]\].

The immune function indicators of patients were evaluated within 7 days after admission, and the dynamic changes in immune function were observed. In previous studies, a decrease in the peripheral blood lymphocyte count was one of the risk factors for secondary fungal infections \[[@CR23]\]. However, this phenomenon was not observed in the present study. Interestingly, the HLA-DR percentage on days 1 and 7 did not differ significantly between the groups. Therefore, the difference in HLA-DR percentage on days 1 and 7 was compared between the two groups in the present study. △HLA-DR (day 7--day 1) was higher in patients with no IPF infection than in those with IPF infection. The decline in HLA-DR indicated impaired immune function secondary to fungal infection in patients with influenza A/H1N1 infection. With constant exposure to fungi, the normal human immune system developed defenses for fungal clearance via alveolar macrophages, neutrophils, monocytes and NK cells \[[@CR24]\]. This study showed that a continuous decline in immune function could lead to the development of IPF infections. In previous studies, most patients with aspergillosis associated with influenza did not have classic risk factors or radiographic findings \[[@CR25]\]. Therefore, dynamic monitoring of immune function may help in the early detection of IPF infection.

Conclusions {#Sec12}
===========

In summary, IPF infection is a potential complication of severe influenza infection. Progressive pulmonary infection after the initial diagnosis of influenza should raise suspicion of super-infection, including with fungi.

This retrospective study had three limitations. First, the lower percentage of histological examinations under bronchoscopic biopsy might have resulted in missed diagnosis in some patients. Second, only 19 patients were identified and the sample size was insufficient to investigate all the features. Finally, how to enhance immune function and its impact on fungal infections need further investigation.

Availability of Data and Materials {#Sec13}
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The dataset used and analyzed in the present study is available from the corresponding author on reasonable request.

Handling Editor: Weida Liu.

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The authors are grateful to all doctors and nurses from the Department of Critical Care Medicine, the Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China, who participated in the study. They also thank Prof Yayi Hou of Nanjing University Medical School and her team for their help and support.

WKY contributed to study design. ZXY, QG and BYZ contributed to data analysis and interpretation and were also involved in the writing of the manuscript. CXC and JT contributed to the writing of the manuscript. All authors read and approved the final manuscript.

The authors received financial support from the National Natural Science Foundation of China (No. 81701953).

The authors declare no competing interests.

This was a retrospective study. No patient consent was required. The study was conducted in accordance with the amended Declaration of Helsinki.

Not applicable.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.